Assignment 7

Generative Models

In [0]:
import tensorflow as tf
tf.enable_eager_execution()

import functools
import matplotlib.pyplot as plt
import numpy as np
import pdb

Downloading code of utils

In [3]:
# Download the class repository
! git clone https://github.com/aamini/introtodeeplearning_labs.git  > /dev/null 2>&1
% cd introtodeeplearning_labs 
! git pull
% cd .. 

!cp -r introtodeeplearning_labs/lab1 lab1
!cp -r /content/lab1/util.py util.py

# Import the necessary class-specific utility files for this lab
import introtodeeplearning_labs as util
/content/introtodeeplearning_labs
Already up to date.
/content

Download Datasets

In [4]:
# Get the training data: both images from CelebA and ImageNet
path_to_training_data = tf.keras.utils.get_file('train_face.h5', 'https://www.dropbox.com/s/l5iqduhe0gwxumq/train_face.h5?dl=1')
Downloading data from https://www.dropbox.com/s/l5iqduhe0gwxumq/train_face.h5?dl=1
1263894528/1263889489 [==============================] - 26s 0us/step
1263902720/1263889489 [==============================] - 26s 0us/step
In [5]:
# Instantiate a TrainingDatasetLoader using the downloaded dataset
loader = util.TrainingDatasetLoader(path_to_training_data)
number_of_training_examples = loader.get_train_size()
(images, labels) = loader.get_batch(100)
Opening /root/.keras/datasets/train_face.h5
Loading data into memory...

Preview dataset

In [6]:
#@title Change the sliders to look at positive and negative training examples! { run: "auto" }

face_images = images[np.where(labels==1)[0]]
not_face_images = images[np.where(labels==0)[0]]

idx_face = 39 #@param {type:"slider", min:0, max:50, step:1}
idx_not_face = 39 #@param {type:"slider", min:0, max:50, step:1}

plt.figure(figsize=(4,2))
plt.subplot(1, 2, 1)
plt.imshow(face_images[idx_face])
plt.title("Face")
plt.grid(False)

plt.subplot(1, 2, 2)
plt.imshow(not_face_images[idx_not_face])
plt.title("Not Face")
plt.grid(False)
In [7]:
#@title Change the sliders to look at positive and negative training examples! { run: "auto" }
ppb = util.PPBFaceEvaluator(skip=4) # create the dataset handler

gender = "female" #@param ["male", "female"]
skin_color = "darker" #@param ["lighter", "darker"]

img = ppb.get_sample_faces_from_demographic(gender, skin_color)
plt.imshow(img)
plt.grid(False)
Downloading data from https://www.dropbox.com/s/l0lp6qxeplumouf/PPB.tar?dl=1
86245376/86241280 [==============================] - 2s 0us/step
86253568/86241280 [==============================] - 2s 0us/step

Build Baseline Model

In [0]:
n_outputs = 1 # number of outputs (i.e., face or not face)
n_filters = 12 # base number of convolutional filters

'''Function to define a standard CNN model'''
def make_standard_classifier():
    Conv2D = functools.partial(tf.keras.layers.Conv2D, padding='same', activation='relu')
    BatchNormalization = tf.keras.layers.BatchNormalization
    Flatten = tf.keras.layers.Flatten
    Dense = functools.partial(tf.keras.layers.Dense, activation='relu')

    model = tf.keras.Sequential([
    	# TODO: define a convolutional layer with n_filters 5x5 filters and 2x2 stride
        Conv2D(n_filters, 5, strides=(2,2)),
        BatchNormalization(),
        
        # TODO: define a convolutional layer with 2*n_filters 5x5 filters and 2x2 stride
        Conv2D(2 * n_filters, 5, strides=(2,2)),
        BatchNormalization(),

        # TODO: define a convolutional layer with 4*n_filters 3x3 filters and 2x2 stride
        Conv2D(4 * n_filters, 3, strides=(2,2)),
        BatchNormalization(),

        # TODO: define a convolutional layer with 6*n_filters 3x3 filters and 1x1 stride
        Conv2D(6 * n_filters, 3, strides=(1,1)),
        BatchNormalization(),

        Flatten(),
        Dense(1, activation=None),
        tf.keras.layers.Dropout(0.5)
    ])
    return model

Training Baseline

In [57]:
standard_classifier = make_standard_classifier()

batch_size = 36
num_epochs = 10  # keep small to run faster
learning_rate = 1e-3

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) # define our optimizer
loss_history = util.LossHistory(smoothing_factor=0.99) # to record the evolution of the loss
plotter = util.PeriodicPlotter(sec=2, scale='semilogy')

# The training loop!
for epoch in range(num_epochs):
  
  custom_msg = util.custom_progress_text("Epoch: %(epoch).0f Loss: %(loss)2.2f")
  bar = util.create_progress_bar(custom_msg)
  
  for idx in bar(range(loader.get_train_size()//batch_size)):
    # First grab a batch of training data and convert the input images to tensors
    x, y = loader.get_batch(batch_size)
    x = tf.convert_to_tensor(x, dtype=tf.float32)
    y = tf.convert_to_tensor(y, dtype=tf.float32)
    
    # GradientTape to record differentiation operations
    with tf.GradientTape() as tape:
      logits = standard_classifier(x) # feed the images into the model
      loss_value = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits) # compute the loss

    custom_msg.update_mapping(epoch=epoch, loss=loss_value.numpy().mean())
    # Backpropagation
    grads = tape.gradient(loss_value, standard_classifier.variables)
    optimizer.apply_gradients(zip(grads, standard_classifier.variables), global_step=tf.train.get_or_create_global_step())

    loss_history.append(loss_value.numpy().mean()) 
    plotter.plot(loss_history.get())
100%|######################################|Time:  0:01:40  Epoch: 9 Loss: 0.00

Evaluate Baseline CNN

In [58]:
# Evaluate on a subset of CelebA+Imagenet
(batch_x, batch_y) = loader.get_batch(5000)
y_pred_standard = tf.round(tf.nn.sigmoid(standard_classifier.predict(batch_x)))
acc_standard = tf.reduce_mean(tf.cast(tf.equal(batch_y, y_pred_standard), tf.float32))
print "Standard CNN accuracy on (potentially biased) training set: {:.4f}".format(acc_standard.numpy())

# Evaluate on PPB dataset (takes ~3 minutes)
standard_cnn_accuracy = []
for skin_color in ['lighter', 'darker']:
  for gender in ['male', 'female']:
    standard_cnn_accuracy.append( ppb.evaluate([standard_classifier], gender, skin_color, from_logit=True)[0] )
    print 
    print "{} {}: {}".format(gender, skin_color, standard_cnn_accuracy[-1])
    
plt.bar(range(4), standard_cnn_accuracy)
plt.xticks(range(4), ('LM', 'LF', 'DM', 'DF'))
plt.ylim(np.min(standard_cnn_accuracy)-0.1,np.max(standard_cnn_accuracy)+0.1)
plt.ylabel('Accuracy')
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/keras/layers/core.py:143: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
N/A% (0 of 97) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
Standard CNN accuracy on (potentially biased) training set: 0.9984
100% (97 of 97) |########################| Elapsed Time: 0:01:09 Time:  0:01:09
N/A% (0 of 72) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male lighter: 0.824742268041
100% (72 of 72) |########################| Elapsed Time: 0:00:49 Time:  0:00:49
N/A% (0 of 78) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
female lighter: 0.861111111111
100% (78 of 78) |########################| Elapsed Time: 0:00:51 Time:  0:00:51
N/A% (0 of 71) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male darker: 0.717948717949
100% (71 of 71) |########################| Elapsed Time: 0:00:45 Time:  0:00:45
female darker: 0.845070422535
Out[58]:
Text(0,0.5,'Accuracy')

We can notice a bias since for lighter skin tone, the accuracy is considerably higher. There might also be a slight bias for females over males.


Vae Loss Function

In [0]:
# Function to calculate VAE loss given an input x, reconstructed output x_pred, 
#    encoded means mu, encoded log of standard deviation logsigma, and weight parameter for the latent loss
def vae_loss_function(x, x_pred, mu, logsigma, kl_weight=0.0005):
  '''TODO: Define the latent loss'''
  latent_loss = 0.5 * tf.reduce_sum(mu**2 - logsigma + tf.exp(logsigma) - 1 )
  '''TODO: Define the reconstruction loss. Hint: you'll need to use tf.reduce_mean'''
  reconstruction_loss = tf.reduce_mean((x - x_pred) ** 2)
  '''TODO: Define the VAE loss'''
  vae_loss = kl_weight * latent_loss + reconstruction_loss
  return vae_loss

Reparameterization and Sampling

In [0]:
"""Reparameterization trick by sampling from an isotropic unit Gaussian.
# Arguments
    args (tensor): mean and log of standard deviation of latent distribution (Q(z|X))
# Returns
    z (tensor): sampled latent vector
"""
def sampling(args):
    z_mean, z_logsigma = args
    batch = z_mean.shape[0]
    dim = z_mean.shape[1]
    
    # by default, random_normal has mean=0 and std=1.0
    epsilon = tf.random_normal(tf.shape(z_mean))
    '''TODO: Define the reparameterization computation!'''
    z = z_mean + tf.exp(0.5 * z_logsigma) * epsilon
    return z

Debiasing Vae Loss Function

In [0]:
# Loss function for DB-VAE
def debiasing_loss_function(x, x_pred, y, y_logit, mu, logsigma):

  '''TODO: call the relevant function to obtain VAE loss'''
  vae_loss = vae_loss_function(x, x_pred, mu, logsigma, kl_weight=0.0005)
  '''TODO: define the classification loss'''
  classification_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=y_logit)
  # Use the training data labels to create variable face_mask
  face_mask = tf.cast(tf.equal(y, 1), tf.float32)
  
  '''TODO: define the DB-VAE total loss! Hint: think about the dimensionality of your output.'''
  total_loss = tf.reduce_mean(classification_loss + face_mask * vae_loss)
  return total_loss, classification_loss

Vae Model Creation

In [0]:
latent_dim = 100

Encoder

In [0]:
'''Define the encoder network for the DB-VAE'''
def make_face_encoder_network():
    Conv2D = functools.partial(tf.keras.layers.Conv2D, padding='same', activation='relu')
    BatchNormalization = tf.keras.layers.BatchNormalization
    Flatten = tf.keras.layers.Flatten
    Dense = functools.partial(tf.keras.layers.Dense, activation='relu')

    inputs = tf.keras.layers.Input(shape=(64,64,3))
    
    hidden = Conv2D(filters=1*n_filters, kernel_size=[5,5],  strides=[2,2])(inputs)
    hidden = BatchNormalization()(hidden)
    hidden = Conv2D(filters=2*n_filters, kernel_size=[5,5],  strides=[2,2])(hidden)
    hidden = BatchNormalization()(hidden)
    hidden = Conv2D(filters=4*n_filters, kernel_size=[3,3],  strides=[2,2])(hidden)
    hidden = BatchNormalization()(hidden)
    hidden = Conv2D(filters=6*n_filters, kernel_size=[3,3],  strides=[1,1])(hidden)
    hidden = BatchNormalization()(hidden)

    hidden = Flatten(name='flatten')(hidden)
    
    '''Encoder outputs:
        y_logit: supervised class prediction
        z_mean: means in the latent space
        z_logsigma: standard deviations in the latent space'''
    y_logit = Dense(1, activation=None, name='y_logit')(hidden)
    z_mean = Dense(latent_dim, name='z_mean')(hidden)
    z_logsigma = Dense(latent_dim, name='z_logsigma')(hidden)

    # use reparameterization trick to sample from the latent space
    z = tf.keras.layers.Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_logsigma])

    # define the outputs that the encoder model should return
    outputs = [y_logit, z_mean, z_logsigma, z]
    # finalize the encoder model
    encoder = tf.keras.Model(inputs=inputs, outputs=outputs, name='encoder')

    # get the shape of the final convolutional output (right before the flatten)
    flatten_layer_idx = encoder.layers.index(encoder.get_layer('flatten'))
    pre_flatten_shape = encoder.layers[flatten_layer_idx-1].get_output_at(0).shape[1:]
    
    return encoder, inputs, outputs, pre_flatten_shape

Decoder

In [0]:
 '''Define the decoder network for the DB-VAE'''
def make_face_decoder_network(pre_flatten_shape):
  Conv2DTranspose = functools.partial(tf.keras.layers.Conv2DTranspose, padding='same', activation='relu')
  BatchNormalization = tf.keras.layers.BatchNormalization
  Flatten = tf.keras.layers.Flatten
  Dense = functools.partial(tf.keras.layers.Dense, activation='relu')

  latent_inputs = tf.keras.layers.Input(shape=(latent_dim,))
  
  hidden = Dense(tf.reduce_prod(pre_flatten_shape))(latent_inputs)
  hidden = tf.keras.layers.Reshape(pre_flatten_shape)(hidden)
  
  # series of deconvolutional layers with batch normalization
  hidden = Conv2DTranspose(filters=4*n_filters, kernel_size=[3,3],  strides=[1,1])(hidden)
  hidden = BatchNormalization()(hidden)
  hidden = Conv2DTranspose(filters=2*n_filters, kernel_size=[3,3],  strides=[2,2])(hidden)
  hidden = BatchNormalization()(hidden)
  hidden = Conv2DTranspose(filters=1*n_filters, kernel_size=[5,5],  strides=[2,2])(hidden)
  hidden = BatchNormalization()(hidden)
  
  x_hat = Conv2DTranspose(filters=3, kernel_size=[5,5], strides=[2,2])(hidden)

  # instantiate decoder model
  decoder = tf.keras.Model(inputs=latent_inputs, outputs=x_hat, name='decoder')
  return decoder
In [50]:
  '''TODO: create the encoder and decoder networks'''
encoder, inputs, outputs, pre_flatten_shape = make_face_encoder_network()
decoder = make_face_decoder_network(pre_flatten_shape)
# initialize the models
encoder_output = encoder(inputs)
y_logit, z_mean, z_logsigma, z = encoder_output
reconstructed_inputs = decoder(z)

vae = tf.keras.Model(inputs, reconstructed_inputs)
util.display_model(encoder)
Out[50]:

VAE Utils

In [0]:
# Function to return the means for an input image batch
def get_latent_mu(images, encoder, batch_size=1024):
    N = images.shape[0]
    mu = np.zeros((N, latent_dim))
    for start_ind in xrange(0, N, batch_size):
        end_ind = min(start_ind+batch_size, N+1)
        batch = images[start_ind:end_ind]
        batch = tf.convert_to_tensor(batch, dtype=tf.float32)/255.
        _, batch_mu, _, _ = encoder(batch)
        mu[start_ind:end_ind] = batch_mu
    return mu
  
'''Function that recomputes the sampling probabilities for images within a batch
    based on how they distribute across the '''
def get_training_sample_probabilities(images, encoder, bins=10, smoothing_fac=0.0): 
    print "Recomputing the sampling probabilities"
    
    mu = get_latent_mu(images, encoder)
    # sampling probabilities for the images
    training_sample_p = np.zeros(mu.shape[0])
    
    # consider the distribution for each latent variable 
    for i in range(latent_dim):
      
        latent_distribution = mu[:,i]
        # generate a histogram of the latent distribution
        hist_density, bin_edges =  np.histogram(latent_distribution, density=True, bins=bins)

        # find which latent bin every data sample falls in 
        # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.digitize.html
        bin_edges[0] = -float('inf')
        bin_edges[-1] = float('inf')
        '''TODO: call the digitize function to find which bins in the latent distribution 
            every data sample falls in to'''
        bin_index = np.digitize(latent_distribution, bin_edges)
        # smooth the density function [Eq. #]
        hist_smoothed_density = hist_density + smoothing_fac
        hist_smoothed_density = hist_smoothed_density / np.sum(hist_smoothed_density)

        '''TODO: invert the density function to compute the sampling probability!
            HINT: think carefully about the indexing of the bins! What is the length of bin_edges?'''
        p = 1 / hist_smoothed_density[bin_index - 1]
        # normalize all probabilities
        p = p / np.sum(p)
        
        # update sampling probabilities 
        training_sample_p = np.maximum(p, training_sample_p)
        
    # final normalization
    training_sample_p /= np.sum(training_sample_p)

    return training_sample_p

VAE Training and unbiased Model

In [55]:
loss_history = []

batch_size = 36
num_epochs = 10  # keep small to run faster
learning_rate = 1e-3

optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)

enable_debiasing = True
all_faces = loader.get_all_train_faces() # parameter from data loader

for epoch in range(num_epochs):
  
  # progress message and bar
  custom_msg = util.custom_progress_text("Epoch: %(epoch).0f   Iter: %(idx).0f   Class Loss: %(class_loss)2.2f   Loss: %(loss)2.2f")
  bar = util.create_progress_bar(custom_msg)

  p_faces = None
  if enable_debiasing: 
      # Recompute data sampling proabilities if debiasing is enabled
      '''TODO: write the function call to recompute the sampling probabilities
          when debiasing is enabled'''
      p_faces = get_training_sample_probabilities(all_faces, encoder, bins=10, smoothing_fac=0.0)
  for idx in bar(range(loader.get_train_size()//batch_size)):
    # load a batch of data
    (x, y) = loader.get_batch(batch_size, p_pos=p_faces)
    x = tf.convert_to_tensor(x, dtype=tf.float32)
    y = tf.convert_to_tensor(y, dtype=tf.float32)
  
    # define GradientTape for automatic differentiation
    with tf.GradientTape() as tape:
      y_logit, mu, logsigma, z = encoder(x)
      x_hat = decoder(z)
      '''TODO: call the relevant loss function to compute the loss'''
      loss, class_loss = debiasing_loss_function(x, x_hat, y, y_logit, mu, logsigma)
      '''TODO: use the GradientTape.gradient method to compute the gradients'''
      grads = tape.gradient(loss, vae.variables)
    # apply gradients to variables
    optimizer.apply_gradients(zip(grads, vae.variables),
                              global_step=tf.train.get_or_create_global_step())

    # track the losses
    class_loss_value = class_loss.numpy().mean()
    loss_value = loss.numpy().mean()
    loss_history.append((class_loss_value, loss_value))
    custom_msg.update_mapping(epoch=epoch, idx=idx, loss=loss_value, class_loss=class_loss_value)
    
    # plot the progress every 100 steps
    if idx%100 == 0: 
      util.plot_sample(x,y,vae)
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:44  Epoch: 0   Iter: 99   Class Loss: 0.02   Loss: 0.05
  6%|     |ETA:   0:02:40  Epoch: 0   Iter: 199   Class Loss: 0.01   Loss: 0.04
  9%|     |ETA:   0:02:32  Epoch: 0   Iter: 298   Class Loss: 0.11   Loss: 0.15
 13%|     |ETA:   0:02:31  Epoch: 0   Iter: 398   Class Loss: 0.07   Loss: 0.11
 16%|     |ETA:   0:02:21  Epoch: 0   Iter: 499   Class Loss: 0.01   Loss: 0.04
 19%|     |ETA:   0:02:17  Epoch: 0   Iter: 599   Class Loss: 0.01   Loss: 0.04
 22%|#    |ETA:   0:02:09  Epoch: 0   Iter: 699   Class Loss: 0.01   Loss: 0.03
 26%|#    |ETA:   0:02:05  Epoch: 0   Iter: 798   Class Loss: 0.07   Loss: 0.11
 29%|#    |ETA:   0:01:57  Epoch: 0   Iter: 898   Class Loss: 0.02   Loss: 0.06
 32%|#    |ETA:   0:01:54  Epoch: 0   Iter: 999   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:48  Epoch: 0   Iter: 1098   Class Loss: 0.13   Loss: 0.16
 39%|#   |ETA:   0:01:57  Epoch: 0   Iter: 1198   Class Loss: 0.06   Loss: 0.09
 42%|#   |ETA:   0:01:52  Epoch: 0   Iter: 1299   Class Loss: 0.03   Loss: 0.07
 45%|#   |ETA:   0:01:27  Epoch: 0   Iter: 1398   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:23  Epoch: 0   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:18  Epoch: 0   Iter: 1599   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:12  Epoch: 0   Iter: 1699   Class Loss: 0.04   Loss: 0.07
 58%|##  |ETA:   0:01:07  Epoch: 0   Iter: 1798   Class Loss: 0.01   Loss: 0.05
 62%|##  |ETA:   0:01:00  Epoch: 0   Iter: 1899   Class Loss: 0.02   Loss: 0.05
 65%|##  |ETA:   0:00:54  Epoch: 0   Iter: 1998   Class Loss: 0.01   Loss: 0.04
 68%|##  |ETA:   0:01:00  Epoch: 0   Iter: 2098   Class Loss: 0.01   Loss: 0.04
 72%|##  |ETA:   0:00:54  Epoch: 0   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:40  Epoch: 0   Iter: 2299   Class Loss: 0.01   Loss: 0.04
 78%|### |ETA:   0:00:34  Epoch: 0   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:30  Epoch: 0   Iter: 2498   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:28  Epoch: 0   Iter: 2599   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:20  Epoch: 0   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:13  Epoch: 0   Iter: 2799   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:08  Epoch: 0   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 0   Iter: 2999   Class Loss: 0.03   Loss: 0.06
100%|####|Time:  0:02:56  Epoch: 0   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:16  Epoch: 1   Iter: 97   Class Loss: 0.34   Loss: 0.37
  6%|     |ETA:   0:02:14  Epoch: 1   Iter: 198   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:08  Epoch: 1   Iter: 298   Class Loss: 0.01   Loss: 0.04
 13%|     |ETA:   0:02:02  Epoch: 1   Iter: 398   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:02:01  Epoch: 1   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:54  Epoch: 1   Iter: 597   Class Loss: 0.01   Loss: 0.04
 22%|#    |ETA:   0:01:49  Epoch: 1   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:45  Epoch: 1   Iter: 799   Class Loss: 0.09   Loss: 0.12
 29%|#    |ETA:   0:01:41  Epoch: 1   Iter: 898   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:52  Epoch: 1   Iter: 998   Class Loss: 0.05   Loss: 0.08
 35%|#   |ETA:   0:01:47  Epoch: 1   Iter: 1098   Class Loss: 0.02   Loss: 0.05
 39%|#   |ETA:   0:01:26  Epoch: 1   Iter: 1198   Class Loss: 0.02   Loss: 0.05
 42%|#   |ETA:   0:01:23  Epoch: 1   Iter: 1297   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:18  Epoch: 1   Iter: 1397   Class Loss: 0.01   Loss: 0.04
 49%|#   |ETA:   0:01:12  Epoch: 1   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:07  Epoch: 1   Iter: 1599   Class Loss: 0.01   Loss: 0.04
 55%|##  |ETA:   0:01:04  Epoch: 1   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:59  Epoch: 1   Iter: 1798   Class Loss: 0.01   Loss: 0.04
 62%|##  |ETA:   0:00:54  Epoch: 1   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:48  Epoch: 1   Iter: 1999   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:44  Epoch: 1   Iter: 2097   Class Loss: 0.00   Loss: 0.04
 72%|##  |ETA:   0:00:40  Epoch: 1   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:35  Epoch: 1   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:30  Epoch: 1   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:26  Epoch: 1   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:21  Epoch: 1   Iter: 2599   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:18  Epoch: 1   Iter: 2698   Class Loss: 0.02   Loss: 0.05
 91%|### |ETA:   0:00:12  Epoch: 1   Iter: 2799   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:07  Epoch: 1   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 1   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:30  Epoch: 1   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:24  Epoch: 2   Iter: 98   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:18  Epoch: 2   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:17  Epoch: 2   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:11  Epoch: 2   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:04  Epoch: 2   Iter: 499   Class Loss: 0.01   Loss: 0.05
 19%|     |ETA:   0:01:59  Epoch: 2   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:55  Epoch: 2   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:51  Epoch: 2   Iter: 797   Class Loss: 0.08   Loss: 0.11
 29%|#    |ETA:   0:01:45  Epoch: 2   Iter: 897   Class Loss: 0.00   Loss: 0.04
 32%|#    |ETA:   0:02:02  Epoch: 2   Iter: 998   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:38  Epoch: 2   Iter: 1098   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:46  Epoch: 2   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:26  Epoch: 2   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:23  Epoch: 2   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:14  Epoch: 2   Iter: 1499   Class Loss: 0.01   Loss: 0.05
 52%|##  |ETA:   0:01:12  Epoch: 2   Iter: 1599   Class Loss: 0.01   Loss: 0.03
 55%|##  |ETA:   0:01:07  Epoch: 2   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:03  Epoch: 2   Iter: 1797   Class Loss: 0.01   Loss: 0.04
 62%|##  |ETA:   0:01:06  Epoch: 2   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:01:05  Epoch: 2   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:47  Epoch: 2   Iter: 2099   Class Loss: 0.03   Loss: 0.06
 72%|##  |ETA:   0:00:41  Epoch: 2   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:37  Epoch: 2   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 2   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 2   Iter: 2498   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:22  Epoch: 2   Iter: 2597   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:20  Epoch: 2   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:14  Epoch: 2   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 2   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 2   Iter: 2999   Class Loss: 0.01   Loss: 0.04
100%|####|Time:  0:02:37  Epoch: 2   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:26  Epoch: 3   Iter: 99   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:21  Epoch: 3   Iter: 199   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:18  Epoch: 3   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:14  Epoch: 3   Iter: 397   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:02:06  Epoch: 3   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:02:02  Epoch: 3   Iter: 599   Class Loss: 0.01   Loss: 0.04
 22%|#    |ETA:   0:01:56  Epoch: 3   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:53  Epoch: 3   Iter: 797   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:48  Epoch: 3   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:44  Epoch: 3   Iter: 997   Class Loss: 0.01   Loss: 0.04
 35%|#   |ETA:   0:01:41  Epoch: 3   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:48  Epoch: 3   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:29  Epoch: 3   Iter: 1297   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:25  Epoch: 3   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:16  Epoch: 3   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:12  Epoch: 3   Iter: 1598   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:08  Epoch: 3   Iter: 1697   Class Loss: 0.09   Loss: 0.12
 58%|##  |ETA:   0:01:03  Epoch: 3   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:57  Epoch: 3   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:54  Epoch: 3   Iter: 1998   Class Loss: 0.14   Loss: 0.18
 68%|##  |ETA:   0:00:48  Epoch: 3   Iter: 2099   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:41  Epoch: 3   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:37  Epoch: 3   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 3   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:29  Epoch: 3   Iter: 2498   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:23  Epoch: 3   Iter: 2599   Class Loss: 0.01   Loss: 0.05
 88%|### |ETA:   0:00:20  Epoch: 3   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:15  Epoch: 3   Iter: 2798   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:07  Epoch: 3   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 3   Iter: 2997   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:37  Epoch: 3   Iter: 3052   Class Loss: 0.02   Loss: 0.05
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:26  Epoch: 4   Iter: 99   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:20  Epoch: 4   Iter: 199   Class Loss: 0.02   Loss: 0.05
  9%|     |ETA:   0:02:17  Epoch: 4   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:11  Epoch: 4   Iter: 397   Class Loss: 0.01   Loss: 0.04
 16%|     |ETA:   0:02:07  Epoch: 4   Iter: 497   Class Loss: 0.03   Loss: 0.06
 19%|     |ETA:   0:02:05  Epoch: 4   Iter: 597   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:02:19  Epoch: 4   Iter: 699   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:53  Epoch: 4   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:51  Epoch: 4   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:44  Epoch: 4   Iter: 998   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:42  Epoch: 4   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:46  Epoch: 4   Iter: 1198   Class Loss: 0.05   Loss: 0.08
 42%|#   |ETA:   0:01:27  Epoch: 4   Iter: 1297   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:22  Epoch: 4   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:16  Epoch: 4   Iter: 1499   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:12  Epoch: 4   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:22  Epoch: 4   Iter: 1699   Class Loss: 0.01   Loss: 0.04
 58%|##  |ETA:   0:01:16  Epoch: 4   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:58  Epoch: 4   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:52  Epoch: 4   Iter: 1999   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:48  Epoch: 4   Iter: 2097   Class Loss: 0.03   Loss: 0.07
 71%|##  |ETA:   0:00:42  Epoch: 4   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:37  Epoch: 4   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 4   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 4   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:22  Epoch: 4   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:20  Epoch: 4   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:14  Epoch: 4   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 4   Iter: 2897   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 4   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:39  Epoch: 4   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:29  Epoch: 5   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:24  Epoch: 5   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:17  Epoch: 5   Iter: 299   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:02:17  Epoch: 5   Iter: 399   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:06  Epoch: 5   Iter: 498   Class Loss: 0.05   Loss: 0.08
 19%|     |ETA:   0:02:03  Epoch: 5   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:54  Epoch: 5   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:53  Epoch: 5   Iter: 798   Class Loss: 0.01   Loss: 0.04
 29%|#    |ETA:   0:01:47  Epoch: 5   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:42  Epoch: 5   Iter: 998   Class Loss: 0.00   Loss: 0.04
 35%|#   |ETA:   0:01:43  Epoch: 5   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:46  Epoch: 5   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:28  Epoch: 5   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:21  Epoch: 5   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:18  Epoch: 5   Iter: 1499   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:11  Epoch: 5   Iter: 1597   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:07  Epoch: 5   Iter: 1698   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:01:01  Epoch: 5   Iter: 1797   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:57  Epoch: 5   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:52  Epoch: 5   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:47  Epoch: 5   Iter: 2097   Class Loss: 0.03   Loss: 0.06
 71%|##  |ETA:   0:00:43  Epoch: 5   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:37  Epoch: 5   Iter: 2299   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:32  Epoch: 5   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 5   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:22  Epoch: 5   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:20  Epoch: 5   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:14  Epoch: 5   Iter: 2798   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:07  Epoch: 5   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 5   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:36  Epoch: 5   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:28  Epoch: 6   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:20  Epoch: 6   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:20  Epoch: 6   Iter: 298   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:02:38  Epoch: 6   Iter: 398   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:02:04  Epoch: 6   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:02:03  Epoch: 6   Iter: 598   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:56  Epoch: 6   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:53  Epoch: 6   Iter: 798   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:46  Epoch: 6   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:42  Epoch: 6   Iter: 999   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:37  Epoch: 6   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:47  Epoch: 6   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:34  Epoch: 6   Iter: 1297   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:22  Epoch: 6   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:34  Epoch: 6   Iter: 1498   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:29  Epoch: 6   Iter: 1598   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:09  Epoch: 6   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:02  Epoch: 6   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:57  Epoch: 6   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:52  Epoch: 6   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:48  Epoch: 6   Iter: 2097   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:42  Epoch: 6   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:37  Epoch: 6   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 6   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:28  Epoch: 6   Iter: 2498   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:22  Epoch: 6   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:20  Epoch: 6   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:14  Epoch: 6   Iter: 2798   Class Loss: 0.05   Loss: 0.08
 94%|### |ETA:   0:00:07  Epoch: 6   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 6   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:39  Epoch: 6   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:25  Epoch: 7   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:23  Epoch: 7   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:15  Epoch: 7   Iter: 299   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:02:11  Epoch: 7   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:06  Epoch: 7   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:02:04  Epoch: 7   Iter: 599   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:02:00  Epoch: 7   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:53  Epoch: 7   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:45  Epoch: 7   Iter: 899   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:40  Epoch: 7   Iter: 998   Class Loss: 0.01   Loss: 0.04
 35%|#   |ETA:   0:01:37  Epoch: 7   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:43  Epoch: 7   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:34  Epoch: 7   Iter: 1299   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:20  Epoch: 7   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:16  Epoch: 7   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:11  Epoch: 7   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:06  Epoch: 7   Iter: 1698   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:03  Epoch: 7   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:57  Epoch: 7   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:51  Epoch: 7   Iter: 1999   Class Loss: 0.02   Loss: 0.05
 68%|##  |ETA:   0:00:46  Epoch: 7   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 72%|##  |ETA:   0:00:41  Epoch: 7   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:37  Epoch: 7   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 7   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 7   Iter: 2498   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:22  Epoch: 7   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:17  Epoch: 7   Iter: 2699   Class Loss: 0.01   Loss: 0.04
 91%|### |ETA:   0:00:14  Epoch: 7   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 7   Iter: 2897   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 7   Iter: 2997   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:35  Epoch: 7   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:55  Epoch: 8   Iter: 99   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:21  Epoch: 8   Iter: 199   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:14  Epoch: 8   Iter: 298   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:08  Epoch: 8   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:06  Epoch: 8   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:02:04  Epoch: 8   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:55  Epoch: 8   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:50  Epoch: 8   Iter: 798   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:47  Epoch: 8   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:40  Epoch: 8   Iter: 998   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:37  Epoch: 8   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:46  Epoch: 8   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:44  Epoch: 8   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:41  Epoch: 8   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:17  Epoch: 8   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:11  Epoch: 8   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:07  Epoch: 8   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:00  Epoch: 8   Iter: 1797   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:55  Epoch: 8   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:53  Epoch: 8   Iter: 1998   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:47  Epoch: 8   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:42  Epoch: 8   Iter: 2197   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:36  Epoch: 8   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:31  Epoch: 8   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:28  Epoch: 8   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:22  Epoch: 8   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:17  Epoch: 8   Iter: 2697   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:14  Epoch: 8   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:08  Epoch: 8   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 8   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:37  Epoch: 8   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:25  Epoch: 9   Iter: 98   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:22  Epoch: 9   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:18  Epoch: 9   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:13  Epoch: 9   Iter: 397   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:07  Epoch: 9   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:02:02  Epoch: 9   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:59  Epoch: 9   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:53  Epoch: 9   Iter: 797   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:46  Epoch: 9   Iter: 898   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:40  Epoch: 9   Iter: 997   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:37  Epoch: 9   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:36  Epoch: 9   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:40  Epoch: 9   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:23  Epoch: 9   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:16  Epoch: 9   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:12  Epoch: 9   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:06  Epoch: 9   Iter: 1699   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:01:01  Epoch: 9   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:56  Epoch: 9   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:52  Epoch: 9   Iter: 1997   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:47  Epoch: 9   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:41  Epoch: 9   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:37  Epoch: 9   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:33  Epoch: 9   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 9   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:21  Epoch: 9   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:17  Epoch: 9   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:14  Epoch: 9   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:09  Epoch: 9   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 9   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:36  Epoch: 9   Iter: 3052   Class Loss: 0.00   Loss: 0.04

Debiased Evaluation

In [59]:
# Evaluate on PPB dataset (takes ~4 minutes)
accuracy_debiased = []
for skin_color in ['lighter', 'darker']:
  for gender in ['male', 'female']:
    accuracy_debiased.append( ppb.evaluate([encoder], gender, skin_color, output_idx=0, from_logit=True)[0] )
    print 
    print "{} {}: {}".format(gender, skin_color, accuracy_debiased[-1])
    
    
bar_width = 0.3
plt.bar(np.arange(4), standard_cnn_accuracy, width=bar_width)
plt.bar(np.arange(4)+bar_width, accuracy_debiased, width=bar_width)
plt.legend(('Standard Classifier','Debiased Classifier (DB-VAE)'))
plt.xticks(np.arange(4), ('LM', 'LF', 'DM', 'DF'))
plt.ylim(np.min([standard_cnn_accuracy,accuracy_debiased])-0.1,1)
plt.ylabel('Accuracy')
100% (97 of 97) |########################| Elapsed Time: 0:01:07 Time:  0:01:07
N/A% (0 of 72) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male lighter: 0.969072164948
100% (72 of 72) |########################| Elapsed Time: 0:00:49 Time:  0:00:49
N/A% (0 of 78) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
female lighter: 1.0
100% (78 of 78) |########################| Elapsed Time: 0:00:52 Time:  0:00:52
N/A% (0 of 71) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male darker: 0.935897435897
100% (71 of 71) |########################| Elapsed Time: 0:00:48 Time:  0:00:48
female darker: 0.971830985915
Out[59]:
Text(0,0.5,'Accuracy')

We can notice how much closer the different genders and skin tone have become, and how our accuracy increased for all.


Trying Different Smoothing Factors

In [0]:
def run_smooth_factor(factor_smooth):
  encoder, inputs, outputs, pre_flatten_shape = make_face_encoder_network()
  decoder = make_face_decoder_network(pre_flatten_shape)
  # initialize the models
  encoder_output = encoder(inputs)
  y_logit, z_mean, z_logsigma, z = encoder_output
  reconstructed_inputs = decoder(z)

  vae = tf.keras.Model(inputs, reconstructed_inputs)
  loss_history = []

  batch_size = 36
  num_epochs = 10  # keep small to run faster
  learning_rate = 1e-3

  optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)

  enable_debiasing = True
  all_faces = loader.get_all_train_faces() # parameter from data loader

  for epoch in range(num_epochs):

    # progress message and bar
    custom_msg = util.custom_progress_text("Epoch: %(epoch).0f   Iter: %(idx).0f   Class Loss: %(class_loss)2.2f   Loss: %(loss)2.2f")
    bar = util.create_progress_bar(custom_msg)

    p_faces = None
    if enable_debiasing: 
        # Recompute data sampling proabilities if debiasing is enabled
        '''TODO: write the function call to recompute the sampling probabilities
            when debiasing is enabled'''
        p_faces = get_training_sample_probabilities(all_faces, encoder, bins=10, smoothing_fac=factor_smooth)
    for idx in bar(range(loader.get_train_size()//batch_size)):
      # load a batch of data
      (x, y) = loader.get_batch(batch_size, p_pos=p_faces)
      x = tf.convert_to_tensor(x, dtype=tf.float32)
      y = tf.convert_to_tensor(y, dtype=tf.float32)

      # define GradientTape for automatic differentiation
      with tf.GradientTape() as tape:
        y_logit, mu, logsigma, z = encoder(x)
        x_hat = decoder(z)
        '''TODO: call the relevant loss function to compute the loss'''
        loss, class_loss = debiasing_loss_function(x, x_hat, y, y_logit, mu, logsigma)
        '''TODO: use the GradientTape.gradient method to compute the gradients'''
        grads = tape.gradient(loss, vae.variables)
      # apply gradients to variables
      optimizer.apply_gradients(zip(grads, vae.variables),
                                global_step=tf.train.get_or_create_global_step())

      # track the losses
      class_loss_value = class_loss.numpy().mean()
      loss_value = loss.numpy().mean()
      loss_history.append((class_loss_value, loss_value))
      custom_msg.update_mapping(epoch=epoch, idx=idx, loss=loss_value, class_loss=class_loss_value)

      # plot the progress every 100 steps
      if idx%100 == 0: 
        util.plot_sample(x,y,vae)
  # Evaluate on PPB dataset (takes ~4 minutes)
  accuracy_debiased = []
  for skin_color in ['lighter', 'darker']:
    for gender in ['male', 'female']:
      accuracy_debiased.append( ppb.evaluate([encoder], gender, skin_color, output_idx=0, from_logit=True)[0] )
      print 
      print "{} {}: {}".format(gender, skin_color, accuracy_debiased[-1])


  bar_width = 0.3
  plt.bar(np.arange(4), standard_cnn_accuracy, width=bar_width)
  plt.bar(np.arange(4)+bar_width, accuracy_debiased, width=bar_width)
  plt.legend(('Standard Classifier','Debiased Classifier (DB-VAE)'))
  plt.xticks(np.arange(4), ('LM', 'LF', 'DM', 'DF'))
  plt.ylim(np.min([standard_cnn_accuracy,accuracy_debiased])-0.1,1)
  plt.ylabel('Accuracy')

Smooth Factor of 0.2

In [61]:
run_smooth_factor(0.2)
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:19  Epoch: 0   Iter: 99   Class Loss: 0.12   Loss: 0.16
  6%|     |ETA:   0:02:15  Epoch: 0   Iter: 199   Class Loss: 0.27   Loss: 0.31
  9%|     |ETA:   0:02:10  Epoch: 0   Iter: 299   Class Loss: 0.08   Loss: 0.12
 13%|     |ETA:   0:02:06  Epoch: 0   Iter: 399   Class Loss: 0.15   Loss: 0.19
 16%|     |ETA:   0:01:58  Epoch: 0   Iter: 498   Class Loss: 0.06   Loss: 0.10
 19%|     |ETA:   0:01:57  Epoch: 0   Iter: 598   Class Loss: 0.03   Loss: 0.07
 22%|#    |ETA:   0:02:06  Epoch: 0   Iter: 699   Class Loss: 0.01   Loss: 0.05
 26%|#    |ETA:   0:01:51  Epoch: 0   Iter: 798   Class Loss: 0.01   Loss: 0.05
 29%|#    |ETA:   0:01:41  Epoch: 0   Iter: 897   Class Loss: 0.00   Loss: 0.04
 32%|#    |ETA:   0:01:36  Epoch: 0   Iter: 998   Class Loss: 0.04   Loss: 0.08
 35%|#   |ETA:   0:01:32  Epoch: 0   Iter: 1097   Class Loss: 0.02   Loss: 0.06
 39%|#   |ETA:   0:01:27  Epoch: 0   Iter: 1197   Class Loss: 0.04   Loss: 0.09
 42%|#   |ETA:   0:01:24  Epoch: 0   Iter: 1298   Class Loss: 0.07   Loss: 0.11
 45%|#   |ETA:   0:01:18  Epoch: 0   Iter: 1399   Class Loss: 0.33   Loss: 0.37
 49%|#   |ETA:   0:01:15  Epoch: 0   Iter: 1497   Class Loss: 0.02   Loss: 0.07
 52%|##  |ETA:   0:01:08  Epoch: 0   Iter: 1599   Class Loss: 0.00   Loss: 0.05
 55%|##  |ETA:   0:01:14  Epoch: 0   Iter: 1698   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:01:10  Epoch: 0   Iter: 1799   Class Loss: 0.01   Loss: 0.05
 62%|##  |ETA:   0:00:55  Epoch: 0   Iter: 1899   Class Loss: 0.05   Loss: 0.09
 65%|##  |ETA:   0:00:49  Epoch: 0   Iter: 1998   Class Loss: 0.01   Loss: 0.06
 68%|##  |ETA:   0:00:44  Epoch: 0   Iter: 2098   Class Loss: 0.01   Loss: 0.04
 72%|##  |ETA:   0:00:42  Epoch: 0   Iter: 2198   Class Loss: 0.00   Loss: 0.05
 75%|### |ETA:   0:00:40  Epoch: 0   Iter: 2298   Class Loss: 0.00   Loss: 0.05
 78%|### |ETA:   0:00:31  Epoch: 0   Iter: 2398   Class Loss: 0.13   Loss: 0.17
 81%|### |ETA:   0:00:26  Epoch: 0   Iter: 2497   Class Loss: 0.00   Loss: 0.05
 85%|### |ETA:   0:00:21  Epoch: 0   Iter: 2597   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:16  Epoch: 0   Iter: 2698   Class Loss: 0.02   Loss: 0.06
 91%|### |ETA:   0:00:12  Epoch: 0   Iter: 2797   Class Loss: 0.00   Loss: 0.05
 94%|### |ETA:   0:00:07  Epoch: 0   Iter: 2898   Class Loss: 0.00   Loss: 0.05
 98%|### |ETA:   0:00:02  Epoch: 0   Iter: 2999   Class Loss: 0.01   Loss: 0.05
100%|####|Time:  0:02:32  Epoch: 0   Iter: 3052   Class Loss: 0.00   Loss: 0.05
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:17  Epoch: 1   Iter: 97   Class Loss: 0.01   Loss: 0.06
  6%|     |ETA:   0:02:14  Epoch: 1   Iter: 199   Class Loss: 0.03   Loss: 0.07
  9%|     |ETA:   0:02:11  Epoch: 1   Iter: 297   Class Loss: 0.01   Loss: 0.05
 13%|     |ETA:   0:02:07  Epoch: 1   Iter: 397   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:02:02  Epoch: 1   Iter: 497   Class Loss: 0.09   Loss: 0.12
 19%|     |ETA:   0:01:56  Epoch: 1   Iter: 599   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:54  Epoch: 1   Iter: 699   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:02:03  Epoch: 1   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:46  Epoch: 1   Iter: 899   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:36  Epoch: 1   Iter: 999   Class Loss: 0.01   Loss: 0.04
 35%|#   |ETA:   0:01:33  Epoch: 1   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:27  Epoch: 1   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:23  Epoch: 1   Iter: 1297   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:19  Epoch: 1   Iter: 1399   Class Loss: 0.09   Loss: 0.12
 49%|#   |ETA:   0:01:15  Epoch: 1   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:09  Epoch: 1   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:04  Epoch: 1   Iter: 1699   Class Loss: 0.01   Loss: 0.05
 58%|##  |ETA:   0:01:00  Epoch: 1   Iter: 1799   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:54  Epoch: 1   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:50  Epoch: 1   Iter: 1999   Class Loss: 0.01   Loss: 0.04
 68%|##  |ETA:   0:00:45  Epoch: 1   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:40  Epoch: 1   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:36  Epoch: 1   Iter: 2297   Class Loss: 0.07   Loss: 0.10
 78%|### |ETA:   0:00:36  Epoch: 1   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:28  Epoch: 1   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:21  Epoch: 1   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:16  Epoch: 1   Iter: 2699   Class Loss: 0.04   Loss: 0.06
 91%|### |ETA:   0:00:12  Epoch: 1   Iter: 2799   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:07  Epoch: 1   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 1   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:32  Epoch: 1   Iter: 3052   Class Loss: 0.03   Loss: 0.06
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:19  Epoch: 2   Iter: 97   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:16  Epoch: 2   Iter: 199   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:11  Epoch: 2   Iter: 298   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:07  Epoch: 2   Iter: 399   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:59  Epoch: 2   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:55  Epoch: 2   Iter: 599   Class Loss: 0.04   Loss: 0.07
 22%|#    |ETA:   0:01:50  Epoch: 2   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:47  Epoch: 2   Iter: 797   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:57  Epoch: 2   Iter: 898   Class Loss: 0.00   Loss: 0.04
 32%|#    |ETA:   0:01:49  Epoch: 2   Iter: 999   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:32  Epoch: 2   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:27  Epoch: 2   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:22  Epoch: 2   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:18  Epoch: 2   Iter: 1397   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:14  Epoch: 2   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:08  Epoch: 2   Iter: 1599   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:16  Epoch: 2   Iter: 1699   Class Loss: 0.01   Loss: 0.04
 58%|##  |ETA:   0:01:11  Epoch: 2   Iter: 1799   Class Loss: 0.07   Loss: 0.10
 62%|##  |ETA:   0:00:54  Epoch: 2   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:50  Epoch: 2   Iter: 1998   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:46  Epoch: 2   Iter: 2099   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:40  Epoch: 2   Iter: 2197   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:35  Epoch: 2   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 2   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:29  Epoch: 2   Iter: 2499   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:22  Epoch: 2   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:16  Epoch: 2   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:12  Epoch: 2   Iter: 2798   Class Loss: 0.01   Loss: 0.04
 94%|### |ETA:   0:00:07  Epoch: 2   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 2   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:34  Epoch: 2   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:18  Epoch: 3   Iter: 97   Class Loss: 0.04   Loss: 0.07
  6%|     |ETA:   0:02:15  Epoch: 3   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:09  Epoch: 3   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:05  Epoch: 3   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:00  Epoch: 3   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:54  Epoch: 3   Iter: 599   Class Loss: 0.01   Loss: 0.03
 22%|#    |ETA:   0:01:51  Epoch: 3   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:46  Epoch: 3   Iter: 798   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:41  Epoch: 3   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:49  Epoch: 3   Iter: 998   Class Loss: 0.00   Loss: 0.04
 35%|#   |ETA:   0:01:42  Epoch: 3   Iter: 1097   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:28  Epoch: 3   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:23  Epoch: 3   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:17  Epoch: 3   Iter: 1399   Class Loss: 0.01   Loss: 0.04
 49%|#   |ETA:   0:01:12  Epoch: 3   Iter: 1497   Class Loss: 0.02   Loss: 0.06
 52%|##  |ETA:   0:01:08  Epoch: 3   Iter: 1597   Class Loss: 0.02   Loss: 0.05
 55%|##  |ETA:   0:01:05  Epoch: 3   Iter: 1698   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:58  Epoch: 3   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:55  Epoch: 3   Iter: 1898   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:50  Epoch: 3   Iter: 1999   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:45  Epoch: 3   Iter: 2097   Class Loss: 0.03   Loss: 0.06
 72%|##  |ETA:   0:00:40  Epoch: 3   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:42  Epoch: 3   Iter: 2298   Class Loss: 0.01   Loss: 0.05
 78%|### |ETA:   0:00:30  Epoch: 3   Iter: 2399   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:26  Epoch: 3   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:24  Epoch: 3   Iter: 2599   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:18  Epoch: 3   Iter: 2699   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:11  Epoch: 3   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 3   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 3   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:32  Epoch: 3   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:19  Epoch: 4   Iter: 99   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:13  Epoch: 4   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:11  Epoch: 4   Iter: 299   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:02:05  Epoch: 4   Iter: 399   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:01  Epoch: 4   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:54  Epoch: 4   Iter: 597   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:50  Epoch: 4   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:45  Epoch: 4   Iter: 798   Class Loss: 0.01   Loss: 0.04
 29%|#    |ETA:   0:01:43  Epoch: 4   Iter: 899   Class Loss: 0.08   Loss: 0.12
 32%|#    |ETA:   0:01:37  Epoch: 4   Iter: 998   Class Loss: 0.00   Loss: 0.04
 35%|#   |ETA:   0:01:46  Epoch: 4   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:39  Epoch: 4   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:25  Epoch: 4   Iter: 1298   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:18  Epoch: 4   Iter: 1397   Class Loss: 0.01   Loss: 0.05
 49%|#   |ETA:   0:01:13  Epoch: 4   Iter: 1497   Class Loss: 0.02   Loss: 0.05
 52%|##  |ETA:   0:01:10  Epoch: 4   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:16  Epoch: 4   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:11  Epoch: 4   Iter: 1799   Class Loss: 0.01   Loss: 0.04
 62%|##  |ETA:   0:00:55  Epoch: 4   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:50  Epoch: 4   Iter: 1999   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:44  Epoch: 4   Iter: 2097   Class Loss: 0.06   Loss: 0.09
 72%|##  |ETA:   0:00:39  Epoch: 4   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:35  Epoch: 4   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:30  Epoch: 4   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:26  Epoch: 4   Iter: 2498   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:21  Epoch: 4   Iter: 2597   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:19  Epoch: 4   Iter: 2698   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:13  Epoch: 4   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 4   Iter: 2897   Class Loss: 0.01   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 4   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:32  Epoch: 4   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:21  Epoch: 5   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:14  Epoch: 5   Iter: 198   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:12  Epoch: 5   Iter: 297   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:02:04  Epoch: 5   Iter: 399   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:01:59  Epoch: 5   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:54  Epoch: 5   Iter: 599   Class Loss: 0.01   Loss: 0.05
 22%|#    |ETA:   0:01:51  Epoch: 5   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:46  Epoch: 5   Iter: 798   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:41  Epoch: 5   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:36  Epoch: 5   Iter: 997   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:32  Epoch: 5   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:39  Epoch: 5   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:35  Epoch: 5   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:18  Epoch: 5   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:12  Epoch: 5   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:08  Epoch: 5   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:03  Epoch: 5   Iter: 1698   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:00  Epoch: 5   Iter: 1797   Class Loss: 0.04   Loss: 0.07
 62%|##  |ETA:   0:00:54  Epoch: 5   Iter: 1897   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:50  Epoch: 5   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:46  Epoch: 5   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:47  Epoch: 5   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:36  Epoch: 5   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:30  Epoch: 5   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:25  Epoch: 5   Iter: 2499   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:21  Epoch: 5   Iter: 2597   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:16  Epoch: 5   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:13  Epoch: 5   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:08  Epoch: 5   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 5   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:32  Epoch: 5   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:17  Epoch: 6   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:13  Epoch: 6   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:10  Epoch: 6   Iter: 298   Class Loss: 0.27   Loss: 0.30
 13%|     |ETA:   0:02:04  Epoch: 6   Iter: 397   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:59  Epoch: 6   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:54  Epoch: 6   Iter: 598   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:51  Epoch: 6   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:44  Epoch: 6   Iter: 799   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:39  Epoch: 6   Iter: 899   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:35  Epoch: 6   Iter: 998   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:31  Epoch: 6   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:26  Epoch: 6   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:28  Epoch: 6   Iter: 1298   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:29  Epoch: 6   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:14  Epoch: 6   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:07  Epoch: 6   Iter: 1598   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:17  Epoch: 6   Iter: 1699   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:01:11  Epoch: 6   Iter: 1799   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:53  Epoch: 6   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:49  Epoch: 6   Iter: 1999   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:44  Epoch: 6   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:40  Epoch: 6   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:36  Epoch: 6   Iter: 2297   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:30  Epoch: 6   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:25  Epoch: 6   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:21  Epoch: 6   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:16  Epoch: 6   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 6   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 6   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 6   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:32  Epoch: 6   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:17  Epoch: 7   Iter: 99   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:12  Epoch: 7   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:08  Epoch: 7   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:03  Epoch: 7   Iter: 397   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:01:58  Epoch: 7   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:54  Epoch: 7   Iter: 598   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:50  Epoch: 7   Iter: 697   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:45  Epoch: 7   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:43  Epoch: 7   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:38  Epoch: 7   Iter: 997   Class Loss: 0.25   Loss: 0.29
 36%|#   |ETA:   0:01:32  Epoch: 7   Iter: 1099   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:26  Epoch: 7   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:22  Epoch: 7   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:18  Epoch: 7   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:24  Epoch: 7   Iter: 1498   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:11  Epoch: 7   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:59  Epoch: 7   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 7   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:50  Epoch: 7   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:46  Epoch: 7   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 7   Iter: 2099   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:43  Epoch: 7   Iter: 2199   Class Loss: 0.13   Loss: 0.16
 75%|### |ETA:   0:00:32  Epoch: 7   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 7   Iter: 2398   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:24  Epoch: 7   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 7   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 7   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 7   Iter: 2797   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 7   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 7   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:25  Epoch: 7   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:27  Epoch: 8   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:22  Epoch: 8   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:00  Epoch: 8   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:55  Epoch: 8   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:52  Epoch: 8   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:47  Epoch: 8   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:43  Epoch: 8   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:38  Epoch: 8   Iter: 799   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:33  Epoch: 8   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:30  Epoch: 8   Iter: 998   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:26  Epoch: 8   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:21  Epoch: 8   Iter: 1198   Class Loss: 0.02   Loss: 0.04
 42%|#   |ETA:   0:01:17  Epoch: 8   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:12  Epoch: 8   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:09  Epoch: 8   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:03  Epoch: 8   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:59  Epoch: 8   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:59  Epoch: 8   Iter: 1797   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:57  Epoch: 8   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:55  Epoch: 8   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:50  Epoch: 8   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:37  Epoch: 8   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:33  Epoch: 8   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 8   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 8   Iter: 2498   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 8   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 8   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 8   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 8   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 8   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:22  Epoch: 8   Iter: 3052   Class Loss: 0.01   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:09  Epoch: 9   Iter: 97   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:05  Epoch: 9   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:01  Epoch: 9   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:00  Epoch: 9   Iter: 399   Class Loss: 0.08   Loss: 0.12
 16%|     |ETA:   0:02:07  Epoch: 9   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:56  Epoch: 9   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:43  Epoch: 9   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:39  Epoch: 9   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:36  Epoch: 9   Iter: 899   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:29  Epoch: 9   Iter: 999   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:25  Epoch: 9   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:22  Epoch: 9   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:17  Epoch: 9   Iter: 1299   Class Loss: 0.01   Loss: 0.04
 45%|#   |ETA:   0:01:12  Epoch: 9   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:08  Epoch: 9   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:04  Epoch: 9   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:00  Epoch: 9   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:56  Epoch: 9   Iter: 1797   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:52  Epoch: 9   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:47  Epoch: 9   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 9   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:43  Epoch: 9   Iter: 2197   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:38  Epoch: 9   Iter: 2298   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:28  Epoch: 9   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:30  Epoch: 9   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:20  Epoch: 9   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 9   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 9   Iter: 2797   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 9   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 9   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:24  Epoch: 9   Iter: 3052   Class Loss: 0.00   Loss: 0.03
100% (97 of 97) |########################| Elapsed Time: 0:01:09 Time:  0:01:09
N/A% (0 of 72) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male lighter: 0.835051546392
100% (72 of 72) |########################| Elapsed Time: 0:00:50 Time:  0:00:50
N/A% (0 of 78) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
female lighter: 0.875
100% (78 of 78) |########################| Elapsed Time: 0:00:52 Time:  0:00:52
N/A% (0 of 71) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male darker: 0.75641025641
100% (71 of 71) |########################| Elapsed Time: 0:00:48 Time:  0:00:48
female darker: 0.830985915493

Smooth Factor of 0.5

In [62]:
run_smooth_factor(0.5)
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:12  Epoch: 0   Iter: 98   Class Loss: 0.16   Loss: 0.19
  6%|     |ETA:   0:02:07  Epoch: 0   Iter: 198   Class Loss: 0.06   Loss: 0.10
  9%|     |ETA:   0:02:02  Epoch: 0   Iter: 299   Class Loss: 0.07   Loss: 0.11
 13%|     |ETA:   0:01:58  Epoch: 0   Iter: 397   Class Loss: 0.13   Loss: 0.17
 16%|     |ETA:   0:01:53  Epoch: 0   Iter: 499   Class Loss: 0.21   Loss: 0.25
 19%|     |ETA:   0:01:49  Epoch: 0   Iter: 599   Class Loss: 0.01   Loss: 0.04
 22%|#    |ETA:   0:01:44  Epoch: 0   Iter: 697   Class Loss: 0.16   Loss: 0.19
 26%|#    |ETA:   0:01:42  Epoch: 0   Iter: 798   Class Loss: 0.08   Loss: 0.11
 29%|#    |ETA:   0:01:37  Epoch: 0   Iter: 897   Class Loss: 0.05   Loss: 0.09
 32%|#    |ETA:   0:01:32  Epoch: 0   Iter: 997   Class Loss: 0.01   Loss: 0.04
 36%|#   |ETA:   0:01:27  Epoch: 0   Iter: 1099   Class Loss: 0.03   Loss: 0.06
 39%|#   |ETA:   0:01:38  Epoch: 0   Iter: 1198   Class Loss: 0.01   Loss: 0.05
 42%|#   |ETA:   0:01:29  Epoch: 0   Iter: 1298   Class Loss: 0.01   Loss: 0.05
 45%|#   |ETA:   0:01:14  Epoch: 0   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:09  Epoch: 0   Iter: 1497   Class Loss: 0.08   Loss: 0.12
 52%|##  |ETA:   0:01:04  Epoch: 0   Iter: 1598   Class Loss: 0.01   Loss: 0.05
 55%|##  |ETA:   0:01:00  Epoch: 0   Iter: 1698   Class Loss: 0.01   Loss: 0.05
 58%|##  |ETA:   0:00:56  Epoch: 0   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:51  Epoch: 0   Iter: 1899   Class Loss: 0.01   Loss: 0.05
 65%|##  |ETA:   0:00:46  Epoch: 0   Iter: 1999   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:42  Epoch: 0   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 72%|##  |ETA:   0:00:38  Epoch: 0   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:33  Epoch: 0   Iter: 2298   Class Loss: 0.02   Loss: 0.06
 78%|### |ETA:   0:00:29  Epoch: 0   Iter: 2399   Class Loss: 0.01   Loss: 0.05
 81%|### |ETA:   0:00:24  Epoch: 0   Iter: 2499   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:20  Epoch: 0   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 0   Iter: 2697   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:11  Epoch: 0   Iter: 2797   Class Loss: 0.05   Loss: 0.09
 94%|### |ETA:   0:00:07  Epoch: 0   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 0   Iter: 2998   Class Loss: 0.02   Loss: 0.05
100%|####|Time:  0:02:23  Epoch: 0   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:12  Epoch: 1   Iter: 97   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:07  Epoch: 1   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:02  Epoch: 1   Iter: 299   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:01:58  Epoch: 1   Iter: 397   Class Loss: 0.01   Loss: 0.04
 16%|     |ETA:   0:01:54  Epoch: 1   Iter: 498   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:01:49  Epoch: 1   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:44  Epoch: 1   Iter: 697   Class Loss: 0.04   Loss: 0.07
 26%|#    |ETA:   0:01:39  Epoch: 1   Iter: 799   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:37  Epoch: 1   Iter: 897   Class Loss: 0.21   Loss: 0.24
 32%|#    |ETA:   0:01:31  Epoch: 1   Iter: 998   Class Loss: 0.04   Loss: 0.07
 36%|#   |ETA:   0:01:27  Epoch: 1   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:39  Epoch: 1   Iter: 1198   Class Loss: 0.01   Loss: 0.04
 42%|#   |ETA:   0:01:28  Epoch: 1   Iter: 1299   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:14  Epoch: 1   Iter: 1399   Class Loss: 0.01   Loss: 0.04
 49%|#   |ETA:   0:01:19  Epoch: 1   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:12  Epoch: 1   Iter: 1599   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:00  Epoch: 1   Iter: 1697   Class Loss: 0.01   Loss: 0.05
 58%|##  |ETA:   0:00:56  Epoch: 1   Iter: 1797   Class Loss: 0.02   Loss: 0.06
 62%|##  |ETA:   0:00:51  Epoch: 1   Iter: 1897   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:46  Epoch: 1   Iter: 1999   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 1   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:38  Epoch: 1   Iter: 2199   Class Loss: 0.01   Loss: 0.04
 75%|### |ETA:   0:00:33  Epoch: 1   Iter: 2297   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:29  Epoch: 1   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 1   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:20  Epoch: 1   Iter: 2598   Class Loss: 0.01   Loss: 0.04
 88%|### |ETA:   0:00:16  Epoch: 1   Iter: 2698   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:11  Epoch: 1   Iter: 2797   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 1   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 1   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:23  Epoch: 1   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:28  Epoch: 2   Iter: 97   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:24  Epoch: 2   Iter: 197   Class Loss: 0.01   Loss: 0.05
  9%|     |ETA:   0:02:03  Epoch: 2   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:58  Epoch: 2   Iter: 397   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:01:53  Epoch: 2   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:49  Epoch: 2   Iter: 598   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:44  Epoch: 2   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:40  Epoch: 2   Iter: 797   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:37  Epoch: 2   Iter: 899   Class Loss: 0.00   Loss: 0.04
 32%|#    |ETA:   0:01:30  Epoch: 2   Iter: 999   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:26  Epoch: 2   Iter: 1099   Class Loss: 0.01   Loss: 0.04
 39%|#   |ETA:   0:01:22  Epoch: 2   Iter: 1197   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:18  Epoch: 2   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:26  Epoch: 2   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:09  Epoch: 2   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:04  Epoch: 2   Iter: 1599   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:01  Epoch: 2   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:04  Epoch: 2   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:58  Epoch: 2   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:47  Epoch: 2   Iter: 1999   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:42  Epoch: 2   Iter: 2097   Class Loss: 0.04   Loss: 0.07
 72%|##  |ETA:   0:00:38  Epoch: 2   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:33  Epoch: 2   Iter: 2298   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:29  Epoch: 2   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 2   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:20  Epoch: 2   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 2   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 2   Iter: 2799   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 2   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 2   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:24  Epoch: 2   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:12  Epoch: 3   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:06  Epoch: 3   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:02  Epoch: 3   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:14  Epoch: 3   Iter: 397   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:02:11  Epoch: 3   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:49  Epoch: 3   Iter: 597   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:44  Epoch: 3   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:40  Epoch: 3   Iter: 799   Class Loss: 0.01   Loss: 0.04
 29%|#    |ETA:   0:01:37  Epoch: 3   Iter: 899   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:31  Epoch: 3   Iter: 999   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:26  Epoch: 3   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:22  Epoch: 3   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:18  Epoch: 3   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:14  Epoch: 3   Iter: 1397   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:19  Epoch: 3   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:18  Epoch: 3   Iter: 1598   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:00  Epoch: 3   Iter: 1699   Class Loss: 0.02   Loss: 0.05
 58%|##  |ETA:   0:00:56  Epoch: 3   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:51  Epoch: 3   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:46  Epoch: 3   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:47  Epoch: 3   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:43  Epoch: 3   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:33  Epoch: 3   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:29  Epoch: 3   Iter: 2398   Class Loss: 0.04   Loss: 0.07
 81%|### |ETA:   0:00:24  Epoch: 3   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:20  Epoch: 3   Iter: 2597   Class Loss: 0.04   Loss: 0.08
 88%|### |ETA:   0:00:15  Epoch: 3   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 3   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 3   Iter: 2898   Class Loss: 0.02   Loss: 0.05
 98%|### |ETA:   0:00:02  Epoch: 3   Iter: 2998   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:25  Epoch: 3   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:10  Epoch: 4   Iter: 97   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:06  Epoch: 4   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:02  Epoch: 4   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:57  Epoch: 4   Iter: 397   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:01:53  Epoch: 4   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:48  Epoch: 4   Iter: 598   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:59  Epoch: 4   Iter: 699   Class Loss: 0.01   Loss: 0.04
 26%|#    |ETA:   0:01:52  Epoch: 4   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:36  Epoch: 4   Iter: 897   Class Loss: 0.08   Loss: 0.11
 32%|#    |ETA:   0:01:31  Epoch: 4   Iter: 997   Class Loss: 0.00   Loss: 0.04
 36%|#   |ETA:   0:01:26  Epoch: 4   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:22  Epoch: 4   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:18  Epoch: 4   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:13  Epoch: 4   Iter: 1398   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:08  Epoch: 4   Iter: 1499   Class Loss: 0.01   Loss: 0.04
 52%|##  |ETA:   0:01:04  Epoch: 4   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:10  Epoch: 4   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:56  Epoch: 4   Iter: 1797   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:50  Epoch: 4   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:46  Epoch: 4   Iter: 1998   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:42  Epoch: 4   Iter: 2097   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:38  Epoch: 4   Iter: 2198   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:33  Epoch: 4   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:33  Epoch: 4   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 4   Iter: 2498   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:20  Epoch: 4   Iter: 2597   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 4   Iter: 2697   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 4   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 4   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 4   Iter: 2998   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:23  Epoch: 4   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:10  Epoch: 5   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:07  Epoch: 5   Iter: 197   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:02  Epoch: 5   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:57  Epoch: 5   Iter: 399   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:54  Epoch: 5   Iter: 499   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:01:48  Epoch: 5   Iter: 597   Class Loss: 0.04   Loss: 0.07
 22%|#    |ETA:   0:01:44  Epoch: 5   Iter: 697   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:40  Epoch: 5   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:36  Epoch: 5   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:44  Epoch: 5   Iter: 998   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:38  Epoch: 5   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:23  Epoch: 5   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:17  Epoch: 5   Iter: 1298   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:14  Epoch: 5   Iter: 1397   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:09  Epoch: 5   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:04  Epoch: 5   Iter: 1599   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:00  Epoch: 5   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:56  Epoch: 5   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:01:02  Epoch: 5   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:56  Epoch: 5   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 5   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:38  Epoch: 5   Iter: 2199   Class Loss: 0.02   Loss: 0.05
 75%|### |ETA:   0:00:33  Epoch: 5   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:29  Epoch: 5   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 5   Iter: 2498   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:20  Epoch: 5   Iter: 2599   Class Loss: 0.01   Loss: 0.04
 88%|### |ETA:   0:00:18  Epoch: 5   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:12  Epoch: 5   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 5   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 5   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:25  Epoch: 5   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:11  Epoch: 6   Iter: 98   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:06  Epoch: 6   Iter: 198   Class Loss: 0.01   Loss: 0.04
  9%|     |ETA:   0:02:02  Epoch: 6   Iter: 298   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:01:58  Epoch: 6   Iter: 398   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:01:54  Epoch: 6   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:48  Epoch: 6   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:44  Epoch: 6   Iter: 697   Class Loss: 0.10   Loss: 0.13
 26%|#    |ETA:   0:01:39  Epoch: 6   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:36  Epoch: 6   Iter: 898   Class Loss: 0.00   Loss: 0.04
 32%|#    |ETA:   0:01:31  Epoch: 6   Iter: 998   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:26  Epoch: 6   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:22  Epoch: 6   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:29  Epoch: 6   Iter: 1299   Class Loss: 0.11   Loss: 0.14
 45%|#   |ETA:   0:01:24  Epoch: 6   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:08  Epoch: 6   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:04  Epoch: 6   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:00  Epoch: 6   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:55  Epoch: 6   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:55  Epoch: 6   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:52  Epoch: 6   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 6   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:37  Epoch: 6   Iter: 2197   Class Loss: 0.02   Loss: 0.05
 75%|### |ETA:   0:00:34  Epoch: 6   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:29  Epoch: 6   Iter: 2398   Class Loss: 0.05   Loss: 0.08
 81%|### |ETA:   0:00:24  Epoch: 6   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:20  Epoch: 6   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 6   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 6   Iter: 2797   Class Loss: 0.08   Loss: 0.11
 94%|### |ETA:   0:00:06  Epoch: 6   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 6   Iter: 2997   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:23  Epoch: 6   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:13  Epoch: 7   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:06  Epoch: 7   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:01  Epoch: 7   Iter: 299   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:01:58  Epoch: 7   Iter: 398   Class Loss: 0.02   Loss: 0.05
 16%|     |ETA:   0:01:53  Epoch: 7   Iter: 499   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:01:48  Epoch: 7   Iter: 597   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:45  Epoch: 7   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:40  Epoch: 7   Iter: 797   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:35  Epoch: 7   Iter: 899   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:33  Epoch: 7   Iter: 998   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:26  Epoch: 7   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:22  Epoch: 7   Iter: 1199   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:18  Epoch: 7   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:13  Epoch: 7   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:09  Epoch: 7   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:10  Epoch: 7   Iter: 1597   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:08  Epoch: 7   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:57  Epoch: 7   Iter: 1797   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:51  Epoch: 7   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:47  Epoch: 7   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 7   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:37  Epoch: 7   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:40  Epoch: 7   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:34  Epoch: 7   Iter: 2398   Class Loss: 0.01   Loss: 0.05
 81%|### |ETA:   0:00:24  Epoch: 7   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:20  Epoch: 7   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 7   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 7   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 7   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 7   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:23  Epoch: 7   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:12  Epoch: 8   Iter: 99   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:23  Epoch: 8   Iter: 197   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:19  Epoch: 8   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:58  Epoch: 8   Iter: 397   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:53  Epoch: 8   Iter: 498   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:01:49  Epoch: 8   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:45  Epoch: 8   Iter: 697   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:40  Epoch: 8   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:35  Epoch: 8   Iter: 899   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:32  Epoch: 8   Iter: 997   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:27  Epoch: 8   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:22  Epoch: 8   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:17  Epoch: 8   Iter: 1297   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:13  Epoch: 8   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:08  Epoch: 8   Iter: 1498   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:04  Epoch: 8   Iter: 1598   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:01:00  Epoch: 8   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:55  Epoch: 8   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:58  Epoch: 8   Iter: 1898   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:53  Epoch: 8   Iter: 1998   Class Loss: 0.03   Loss: 0.07
 68%|##  |ETA:   0:00:43  Epoch: 8   Iter: 2098   Class Loss: 0.08   Loss: 0.11
 72%|##  |ETA:   0:00:44  Epoch: 8   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:34  Epoch: 8   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:29  Epoch: 8   Iter: 2398   Class Loss: 0.08   Loss: 0.12
 81%|### |ETA:   0:00:24  Epoch: 8   Iter: 2498   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:20  Epoch: 8   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 8   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 8   Iter: 2799   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 8   Iter: 2898   Class Loss: 0.01   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 8   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:24  Epoch: 8   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:12  Epoch: 9   Iter: 98   Class Loss: 0.01   Loss: 0.04
  6%|     |ETA:   0:02:06  Epoch: 9   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:02  Epoch: 9   Iter: 297   Class Loss: 0.01   Loss: 0.04
 13%|     |ETA:   0:01:57  Epoch: 9   Iter: 397   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:02:07  Epoch: 9   Iter: 498   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:02:06  Epoch: 9   Iter: 598   Class Loss: 0.01   Loss: 0.04
 22%|#    |ETA:   0:01:44  Epoch: 9   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:40  Epoch: 9   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:35  Epoch: 9   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:33  Epoch: 9   Iter: 998   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:27  Epoch: 9   Iter: 1097   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:22  Epoch: 9   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:18  Epoch: 9   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:14  Epoch: 9   Iter: 1398   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:10  Epoch: 9   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:04  Epoch: 9   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:00  Epoch: 9   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:55  Epoch: 9   Iter: 1798   Class Loss: 0.01   Loss: 0.04
 62%|##  |ETA:   0:00:52  Epoch: 9   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:46  Epoch: 9   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 9   Iter: 2097   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:40  Epoch: 9   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:38  Epoch: 9   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:30  Epoch: 9   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 9   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:21  Epoch: 9   Iter: 2599   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:18  Epoch: 9   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:12  Epoch: 9   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 9   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 9   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:24  Epoch: 9   Iter: 3052   Class Loss: 0.00   Loss: 0.04
100% (97 of 97) |########################| Elapsed Time: 0:01:09 Time:  0:01:09
N/A% (0 of 72) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male lighter: 0.979381443299
100% (72 of 72) |########################| Elapsed Time: 0:00:49 Time:  0:00:49
N/A% (0 of 78) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
female lighter: 0.930555555556
100% (78 of 78) |########################| Elapsed Time: 0:00:52 Time:  0:00:52
N/A% (0 of 71) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male darker: 0.820512820513
100% (71 of 71) |########################| Elapsed Time: 0:00:48 Time:  0:00:48
female darker: 0.915492957746

Smooth Factor of 1

In [63]:
run_smooth_factor(1)
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:09  Epoch: 0   Iter: 97   Class Loss: 0.13   Loss: 0.17
  6%|     |ETA:   0:02:06  Epoch: 0   Iter: 199   Class Loss: 0.08   Loss: 0.11
  9%|     |ETA:   0:02:00  Epoch: 0   Iter: 297   Class Loss: 0.18   Loss: 0.22
 13%|     |ETA:   0:01:55  Epoch: 0   Iter: 398   Class Loss: 0.13   Loss: 0.17
 16%|     |ETA:   0:01:51  Epoch: 0   Iter: 499   Class Loss: 0.16   Loss: 0.20
 19%|     |ETA:   0:01:47  Epoch: 0   Iter: 599   Class Loss: 0.01   Loss: 0.06
 22%|#    |ETA:   0:01:43  Epoch: 0   Iter: 698   Class Loss: 0.03   Loss: 0.06
 26%|#    |ETA:   0:01:38  Epoch: 0   Iter: 799   Class Loss: 0.15   Loss: 0.18
 29%|#    |ETA:   0:01:35  Epoch: 0   Iter: 897   Class Loss: 0.04   Loss: 0.08
 32%|#    |ETA:   0:01:30  Epoch: 0   Iter: 999   Class Loss: 0.01   Loss: 0.04
 36%|#   |ETA:   0:01:26  Epoch: 0   Iter: 1099   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:21  Epoch: 0   Iter: 1197   Class Loss: 0.01   Loss: 0.04
 42%|#   |ETA:   0:01:26  Epoch: 0   Iter: 1297   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:27  Epoch: 0   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:22  Epoch: 0   Iter: 1498   Class Loss: 0.01   Loss: 0.05
 52%|##  |ETA:   0:01:04  Epoch: 0   Iter: 1598   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:00:59  Epoch: 0   Iter: 1699   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:00:54  Epoch: 0   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:50  Epoch: 0   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:46  Epoch: 0   Iter: 1997   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:41  Epoch: 0   Iter: 2098   Class Loss: 0.01   Loss: 0.05
 72%|##  |ETA:   0:00:37  Epoch: 0   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 0   Iter: 2297   Class Loss: 0.01   Loss: 0.06
 78%|### |ETA:   0:00:28  Epoch: 0   Iter: 2398   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:24  Epoch: 0   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 0   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 0   Iter: 2698   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:11  Epoch: 0   Iter: 2797   Class Loss: 0.07   Loss: 0.10
 94%|### |ETA:   0:00:06  Epoch: 0   Iter: 2899   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 0   Iter: 2998   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:21  Epoch: 0   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:07  Epoch: 1   Iter: 98   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:04  Epoch: 1   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:01:59  Epoch: 1   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:55  Epoch: 1   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:51  Epoch: 1   Iter: 499   Class Loss: 0.01   Loss: 0.05
 19%|     |ETA:   0:01:47  Epoch: 1   Iter: 597   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:42  Epoch: 1   Iter: 698   Class Loss: 0.09   Loss: 0.12
 26%|#    |ETA:   0:01:37  Epoch: 1   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:34  Epoch: 1   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:29  Epoch: 1   Iter: 999   Class Loss: 0.00   Loss: 0.04
 36%|#   |ETA:   0:01:24  Epoch: 1   Iter: 1099   Class Loss: 0.01   Loss: 0.04
 39%|#   |ETA:   0:01:36  Epoch: 1   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:16  Epoch: 1   Iter: 1299   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:12  Epoch: 1   Iter: 1397   Class Loss: 0.06   Loss: 0.10
 49%|#   |ETA:   0:01:07  Epoch: 1   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:12  Epoch: 1   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:07  Epoch: 1   Iter: 1697   Class Loss: 0.06   Loss: 0.10
 58%|##  |ETA:   0:00:54  Epoch: 1   Iter: 1797   Class Loss: 0.02   Loss: 0.05
 62%|##  |ETA:   0:00:50  Epoch: 1   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:46  Epoch: 1   Iter: 1997   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:41  Epoch: 1   Iter: 2097   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:39  Epoch: 1   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:33  Epoch: 1   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 1   Iter: 2399   Class Loss: 0.02   Loss: 0.05
 81%|### |ETA:   0:00:24  Epoch: 1   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 1   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 1   Iter: 2698   Class Loss: 0.02   Loss: 0.05
 91%|### |ETA:   0:00:11  Epoch: 1   Iter: 2797   Class Loss: 0.16   Loss: 0.19
 94%|### |ETA:   0:00:06  Epoch: 1   Iter: 2897   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 1   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:20  Epoch: 1   Iter: 3052   Class Loss: 0.01   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:08  Epoch: 2   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:04  Epoch: 2   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:16  Epoch: 2   Iter: 299   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:02:12  Epoch: 2   Iter: 397   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:52  Epoch: 2   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:47  Epoch: 2   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:43  Epoch: 2   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:38  Epoch: 2   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:34  Epoch: 2   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:30  Epoch: 2   Iter: 999   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:25  Epoch: 2   Iter: 1097   Class Loss: 0.02   Loss: 0.05
 39%|#   |ETA:   0:01:21  Epoch: 2   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:17  Epoch: 2   Iter: 1297   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:12  Epoch: 2   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:08  Epoch: 2   Iter: 1497   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:03  Epoch: 2   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:59  Epoch: 2   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:55  Epoch: 2   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:53  Epoch: 2   Iter: 1899   Class Loss: 0.12   Loss: 0.16
 65%|##  |ETA:   0:00:55  Epoch: 2   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:48  Epoch: 2   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:37  Epoch: 2   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 2   Iter: 2298   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:28  Epoch: 2   Iter: 2399   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:24  Epoch: 2   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 2   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 2   Iter: 2698   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:11  Epoch: 2   Iter: 2797   Class Loss: 0.02   Loss: 0.05
 94%|### |ETA:   0:00:06  Epoch: 2   Iter: 2897   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 2   Iter: 2998   Class Loss: 0.01   Loss: 0.04
100%|####|Time:  0:02:21  Epoch: 2   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:10  Epoch: 3   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:04  Epoch: 3   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:01  Epoch: 3   Iter: 298   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:56  Epoch: 3   Iter: 398   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:01:54  Epoch: 3   Iter: 497   Class Loss: 0.02   Loss: 0.05
 19%|     |ETA:   0:01:56  Epoch: 3   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:57  Epoch: 3   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:42  Epoch: 3   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:34  Epoch: 3   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:30  Epoch: 3   Iter: 998   Class Loss: 0.00   Loss: 0.04
 36%|#   |ETA:   0:01:25  Epoch: 3   Iter: 1099   Class Loss: 0.02   Loss: 0.05
 39%|#   |ETA:   0:01:21  Epoch: 3   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:16  Epoch: 3   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:12  Epoch: 3   Iter: 1397   Class Loss: 0.02   Loss: 0.06
 49%|#   |ETA:   0:01:08  Epoch: 3   Iter: 1497   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:15  Epoch: 3   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:59  Epoch: 3   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:55  Epoch: 3   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:50  Epoch: 3   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:46  Epoch: 3   Iter: 1999   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 3   Iter: 2098   Class Loss: 0.13   Loss: 0.16
 71%|##  |ETA:   0:00:37  Epoch: 3   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:33  Epoch: 3   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 3   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 3   Iter: 2499   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:19  Epoch: 3   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 3   Iter: 2697   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 3   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 3   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 3   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:21  Epoch: 3   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:10  Epoch: 4   Iter: 97   Class Loss: 0.16   Loss: 0.20
  6%|     |ETA:   0:02:05  Epoch: 4   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:00  Epoch: 4   Iter: 299   Class Loss: 0.02   Loss: 0.05
 13%|     |ETA:   0:01:56  Epoch: 4   Iter: 399   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:52  Epoch: 4   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:47  Epoch: 4   Iter: 597   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:43  Epoch: 4   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:40  Epoch: 4   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:34  Epoch: 4   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:43  Epoch: 4   Iter: 997   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:38  Epoch: 4   Iter: 1099   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:21  Epoch: 4   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:17  Epoch: 4   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:12  Epoch: 4   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:08  Epoch: 4   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:03  Epoch: 4   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 4   Iter: 1699   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:00:54  Epoch: 4   Iter: 1799   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:50  Epoch: 4   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:46  Epoch: 4   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 4   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:37  Epoch: 4   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 4   Iter: 2299   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:31  Epoch: 4   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:29  Epoch: 4   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:21  Epoch: 4   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:17  Epoch: 4   Iter: 2698   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:12  Epoch: 4   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 4   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 4   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:22  Epoch: 4   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:10  Epoch: 5   Iter: 97   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:04  Epoch: 5   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:01  Epoch: 5   Iter: 298   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:57  Epoch: 5   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:52  Epoch: 5   Iter: 499   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:01:49  Epoch: 5   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:43  Epoch: 5   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:38  Epoch: 5   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:35  Epoch: 5   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:30  Epoch: 5   Iter: 997   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:25  Epoch: 5   Iter: 1099   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:20  Epoch: 5   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:16  Epoch: 5   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:22  Epoch: 5   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:17  Epoch: 5   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:04  Epoch: 5   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:59  Epoch: 5   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 5   Iter: 1798   Class Loss: 0.02   Loss: 0.06
 62%|##  |ETA:   0:00:51  Epoch: 5   Iter: 1898   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:54  Epoch: 5   Iter: 1998   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:42  Epoch: 5   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:37  Epoch: 5   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:33  Epoch: 5   Iter: 2297   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 5   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 5   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 5   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 5   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 5   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 5   Iter: 2898   Class Loss: 0.01   Loss: 0.05
 98%|### |ETA:   0:00:02  Epoch: 5   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:20  Epoch: 5   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:29  Epoch: 6   Iter: 98   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:06  Epoch: 6   Iter: 199   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:01  Epoch: 6   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:56  Epoch: 6   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:53  Epoch: 6   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:47  Epoch: 6   Iter: 599   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:43  Epoch: 6   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:38  Epoch: 6   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:34  Epoch: 6   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:29  Epoch: 6   Iter: 998   Class Loss: 0.00   Loss: 0.04
 35%|#   |ETA:   0:01:25  Epoch: 6   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:21  Epoch: 6   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:16  Epoch: 6   Iter: 1298   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:12  Epoch: 6   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:07  Epoch: 6   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:03  Epoch: 6   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:04  Epoch: 6   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:01:03  Epoch: 6   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:52  Epoch: 6   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:46  Epoch: 6   Iter: 1999   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 6   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:37  Epoch: 6   Iter: 2199   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:33  Epoch: 6   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 6   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 6   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 6   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 6   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 6   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 6   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 6   Iter: 2999   Class Loss: 0.11   Loss: 0.15
100%|####|Time:  0:02:22  Epoch: 6   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:08  Epoch: 7   Iter: 99   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:04  Epoch: 7   Iter: 198   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:02  Epoch: 7   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:11  Epoch: 7   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:02  Epoch: 7   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:47  Epoch: 7   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:43  Epoch: 7   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:38  Epoch: 7   Iter: 798   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:33  Epoch: 7   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:28  Epoch: 7   Iter: 998   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:24  Epoch: 7   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:21  Epoch: 7   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:16  Epoch: 7   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:12  Epoch: 7   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:08  Epoch: 7   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:03  Epoch: 7   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:59  Epoch: 7   Iter: 1697   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:00:54  Epoch: 7   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:50  Epoch: 7   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:46  Epoch: 7   Iter: 1997   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:47  Epoch: 7   Iter: 2097   Class Loss: 0.00   Loss: 0.04
 72%|##  |ETA:   0:00:42  Epoch: 7   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:34  Epoch: 7   Iter: 2299   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:31  Epoch: 7   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:24  Epoch: 7   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 7   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 7   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 7   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 7   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 7   Iter: 2998   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:20  Epoch: 7   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:08  Epoch: 8   Iter: 99   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:05  Epoch: 8   Iter: 198   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:01:59  Epoch: 8   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:55  Epoch: 8   Iter: 397   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:51  Epoch: 8   Iter: 499   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:01:46  Epoch: 8   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:47  Epoch: 8   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:51  Epoch: 8   Iter: 799   Class Loss: 0.11   Loss: 0.15
 29%|#    |ETA:   0:01:38  Epoch: 8   Iter: 898   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:29  Epoch: 8   Iter: 999   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:24  Epoch: 8   Iter: 1097   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:21  Epoch: 8   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:16  Epoch: 8   Iter: 1298   Class Loss: 0.01   Loss: 0.04
 45%|#   |ETA:   0:01:12  Epoch: 8   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:08  Epoch: 8   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:03  Epoch: 8   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 8   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 8   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:49  Epoch: 8   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:45  Epoch: 8   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 8   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:37  Epoch: 8   Iter: 2199   Class Loss: 0.02   Loss: 0.04
 75%|### |ETA:   0:00:32  Epoch: 8   Iter: 2297   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:28  Epoch: 8   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:27  Epoch: 8   Iter: 2498   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:22  Epoch: 8   Iter: 2599   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 8   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:11  Epoch: 8   Iter: 2799   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 8   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 8   Iter: 2998   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:20  Epoch: 8   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:09  Epoch: 9   Iter: 97   Class Loss: 0.02   Loss: 0.05
  6%|     |ETA:   0:02:04  Epoch: 9   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:02:09  Epoch: 9   Iter: 298   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:20  Epoch: 9   Iter: 398   Class Loss: 0.00   Loss: 0.02
 16%|     |ETA:   0:01:59  Epoch: 9   Iter: 498   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:01:46  Epoch: 9   Iter: 599   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:42  Epoch: 9   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:38  Epoch: 9   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:33  Epoch: 9   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:29  Epoch: 9   Iter: 997   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:37  Epoch: 9   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:32  Epoch: 9   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:16  Epoch: 9   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:11  Epoch: 9   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:07  Epoch: 9   Iter: 1499   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:03  Epoch: 9   Iter: 1599   Class Loss: 0.00   Loss: 0.04
 55%|##  |ETA:   0:00:59  Epoch: 9   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 9   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:50  Epoch: 9   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:45  Epoch: 9   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 9   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:37  Epoch: 9   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 9   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 9   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:23  Epoch: 9   Iter: 2499   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:19  Epoch: 9   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 9   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:13  Epoch: 9   Iter: 2798   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:07  Epoch: 9   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 9   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:21  Epoch: 9   Iter: 3052   Class Loss: 0.00   Loss: 0.03
100% (97 of 97) |########################| Elapsed Time: 0:01:06 Time:  0:01:06
N/A% (0 of 72) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male lighter: 0.886597938144
100% (72 of 72) |########################| Elapsed Time: 0:00:50 Time:  0:00:50
N/A% (0 of 78) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
female lighter: 0.875
100% (78 of 78) |########################| Elapsed Time: 0:00:52 Time:  0:00:52
N/A% (0 of 71) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male darker: 0.717948717949
100% (71 of 71) |########################| Elapsed Time: 0:00:47 Time:  0:00:47
female darker: 0.816901408451

Smooth Factor of 2

In [64]:
run_smooth_factor(2)
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:07  Epoch: 0   Iter: 98   Class Loss: 0.02   Loss: 0.06
  6%|     |ETA:   0:02:11  Epoch: 0   Iter: 199   Class Loss: 0.02   Loss: 0.06
  9%|     |ETA:   0:02:18  Epoch: 0   Iter: 297   Class Loss: 0.03   Loss: 0.07
 13%|     |ETA:   0:02:04  Epoch: 0   Iter: 398   Class Loss: 0.08   Loss: 0.12
 16%|     |ETA:   0:01:51  Epoch: 0   Iter: 499   Class Loss: 0.02   Loss: 0.05
 19%|     |ETA:   0:01:47  Epoch: 0   Iter: 599   Class Loss: 0.03   Loss: 0.06
 22%|#    |ETA:   0:01:43  Epoch: 0   Iter: 698   Class Loss: 0.01   Loss: 0.05
 26%|#    |ETA:   0:01:39  Epoch: 0   Iter: 799   Class Loss: 0.24   Loss: 0.27
 29%|#    |ETA:   0:01:34  Epoch: 0   Iter: 897   Class Loss: 0.07   Loss: 0.11
 32%|#    |ETA:   0:01:29  Epoch: 0   Iter: 999   Class Loss: 0.00   Loss: 0.04
 35%|#   |ETA:   0:01:25  Epoch: 0   Iter: 1097   Class Loss: 0.01   Loss: 0.05
 39%|#   |ETA:   0:01:20  Epoch: 0   Iter: 1198   Class Loss: 0.01   Loss: 0.04
 42%|#   |ETA:   0:01:17  Epoch: 0   Iter: 1298   Class Loss: 0.02   Loss: 0.06
 45%|#   |ETA:   0:01:12  Epoch: 0   Iter: 1397   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:13  Epoch: 0   Iter: 1498   Class Loss: 0.01   Loss: 0.04
 52%|##  |ETA:   0:01:08  Epoch: 0   Iter: 1599   Class Loss: 0.03   Loss: 0.06
 55%|##  |ETA:   0:00:58  Epoch: 0   Iter: 1697   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:00:54  Epoch: 0   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:50  Epoch: 0   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:52  Epoch: 0   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:47  Epoch: 0   Iter: 2097   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:37  Epoch: 0   Iter: 2197   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:32  Epoch: 0   Iter: 2298   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:28  Epoch: 0   Iter: 2398   Class Loss: 0.01   Loss: 0.04
 81%|### |ETA:   0:00:24  Epoch: 0   Iter: 2499   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:19  Epoch: 0   Iter: 2598   Class Loss: 0.03   Loss: 0.07
 88%|### |ETA:   0:00:15  Epoch: 0   Iter: 2698   Class Loss: 0.02   Loss: 0.06
 91%|### |ETA:   0:00:11  Epoch: 0   Iter: 2797   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 0   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 0   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:20  Epoch: 0   Iter: 3052   Class Loss: 0.01   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:07  Epoch: 1   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:04  Epoch: 1   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:01:59  Epoch: 1   Iter: 297   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:01:55  Epoch: 1   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:50  Epoch: 1   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:53  Epoch: 1   Iter: 597   Class Loss: 0.02   Loss: 0.05
 22%|#    |ETA:   0:01:58  Epoch: 1   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:44  Epoch: 1   Iter: 799   Class Loss: 0.01   Loss: 0.04
 29%|#    |ETA:   0:01:32  Epoch: 1   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:28  Epoch: 1   Iter: 997   Class Loss: 0.05   Loss: 0.08
 36%|#   |ETA:   0:01:24  Epoch: 1   Iter: 1099   Class Loss: 0.01   Loss: 0.04
 39%|#   |ETA:   0:01:20  Epoch: 1   Iter: 1198   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:16  Epoch: 1   Iter: 1299   Class Loss: 0.01   Loss: 0.03
 45%|#   |ETA:   0:01:11  Epoch: 1   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:07  Epoch: 1   Iter: 1497   Class Loss: 0.03   Loss: 0.06
 52%|##  |ETA:   0:01:03  Epoch: 1   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 1   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 1   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:50  Epoch: 1   Iter: 1899   Class Loss: 0.15   Loss: 0.18
 65%|##  |ETA:   0:00:45  Epoch: 1   Iter: 1999   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 1   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:36  Epoch: 1   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 1   Iter: 2297   Class Loss: 0.00   Loss: 0.04
 78%|### |ETA:   0:00:32  Epoch: 1   Iter: 2399   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:28  Epoch: 1   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 1   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 1   Iter: 2698   Class Loss: 0.03   Loss: 0.07
 91%|### |ETA:   0:00:11  Epoch: 1   Iter: 2797   Class Loss: 0.00   Loss: 0.04
 94%|### |ETA:   0:00:08  Epoch: 1   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 1   Iter: 2997   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:21  Epoch: 1   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:06  Epoch: 2   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:03  Epoch: 2   Iter: 199   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:01:58  Epoch: 2   Iter: 297   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:01:53  Epoch: 2   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:49  Epoch: 2   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:45  Epoch: 2   Iter: 597   Class Loss: 0.01   Loss: 0.04
 22%|#    |ETA:   0:01:41  Epoch: 2   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:36  Epoch: 2   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:32  Epoch: 2   Iter: 897   Class Loss: 0.01   Loss: 0.04
 32%|#    |ETA:   0:01:31  Epoch: 2   Iter: 997   Class Loss: 0.00   Loss: 0.03
 35%|#   |ETA:   0:01:37  Epoch: 2   Iter: 1098   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:28  Epoch: 2   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:14  Epoch: 2   Iter: 1298   Class Loss: 0.10   Loss: 0.14
 45%|#   |ETA:   0:01:10  Epoch: 2   Iter: 1399   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:06  Epoch: 2   Iter: 1497   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:02  Epoch: 2   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:57  Epoch: 2   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 2   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:49  Epoch: 2   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:54  Epoch: 2   Iter: 1998   Class Loss: 0.00   Loss: 0.04
 68%|##  |ETA:   0:00:41  Epoch: 2   Iter: 2098   Class Loss: 0.04   Loss: 0.07
 71%|##  |ETA:   0:00:36  Epoch: 2   Iter: 2197   Class Loss: 0.02   Loss: 0.05
 75%|### |ETA:   0:00:32  Epoch: 2   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 2   Iter: 2399   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:24  Epoch: 2   Iter: 2497   Class Loss: 0.00   Loss: 0.04
 85%|### |ETA:   0:00:19  Epoch: 2   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 2   Iter: 2697   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:12  Epoch: 2   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 2   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 2   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:18  Epoch: 2   Iter: 3052   Class Loss: 0.01   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:06  Epoch: 3   Iter: 98   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:02  Epoch: 3   Iter: 198   Class Loss: 0.02   Loss: 0.05
  9%|     |ETA:   0:01:58  Epoch: 3   Iter: 299   Class Loss: 0.01   Loss: 0.04
 13%|     |ETA:   0:01:53  Epoch: 3   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:49  Epoch: 3   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:45  Epoch: 3   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:41  Epoch: 3   Iter: 698   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:37  Epoch: 3   Iter: 798   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:32  Epoch: 3   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:28  Epoch: 3   Iter: 997   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:24  Epoch: 3   Iter: 1099   Class Loss: 0.05   Loss: 0.08
 39%|#   |ETA:   0:01:19  Epoch: 3   Iter: 1198   Class Loss: 0.01   Loss: 0.04
 42%|#   |ETA:   0:01:15  Epoch: 3   Iter: 1299   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:11  Epoch: 3   Iter: 1397   Class Loss: 0.00   Loss: 0.04
 49%|#   |ETA:   0:01:17  Epoch: 3   Iter: 1497   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:12  Epoch: 3   Iter: 1598   Class Loss: 0.10   Loss: 0.13
 55%|##  |ETA:   0:00:58  Epoch: 3   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 3   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:49  Epoch: 3   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:45  Epoch: 3   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 3   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:36  Epoch: 3   Iter: 2197   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:32  Epoch: 3   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:27  Epoch: 3   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:23  Epoch: 3   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 3   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 3   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:10  Epoch: 3   Iter: 2799   Class Loss: 0.01   Loss: 0.04
 94%|### |ETA:   0:00:06  Epoch: 3   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 3   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:17  Epoch: 3   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:07  Epoch: 4   Iter: 98   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:20  Epoch: 4   Iter: 199   Class Loss: 0.00   Loss: 0.04
  9%|     |ETA:   0:02:16  Epoch: 4   Iter: 298   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:18  Epoch: 4   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:02:05  Epoch: 4   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:44  Epoch: 4   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:40  Epoch: 4   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:36  Epoch: 4   Iter: 799   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:32  Epoch: 4   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:27  Epoch: 4   Iter: 999   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:23  Epoch: 4   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:19  Epoch: 4   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:15  Epoch: 4   Iter: 1298   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:11  Epoch: 4   Iter: 1399   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:06  Epoch: 4   Iter: 1498   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:02  Epoch: 4   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 4   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:53  Epoch: 4   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:57  Epoch: 4   Iter: 1898   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:52  Epoch: 4   Iter: 1999   Class Loss: 0.01   Loss: 0.04
 68%|##  |ETA:   0:00:40  Epoch: 4   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:36  Epoch: 4   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 4   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 4   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:28  Epoch: 4   Iter: 2499   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 4   Iter: 2598   Class Loss: 0.02   Loss: 0.05
 88%|### |ETA:   0:00:15  Epoch: 4   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:10  Epoch: 4   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 4   Iter: 2898   Class Loss: 0.00   Loss: 0.04
 98%|### |ETA:   0:00:02  Epoch: 4   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:21  Epoch: 4   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:07  Epoch: 5   Iter: 97   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:03  Epoch: 5   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:01:58  Epoch: 5   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:53  Epoch: 5   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:51  Epoch: 5   Iter: 498   Class Loss: 0.00   Loss: 0.04
 19%|     |ETA:   0:02:02  Epoch: 5   Iter: 599   Class Loss: 0.00   Loss: 0.04
 22%|#    |ETA:   0:01:52  Epoch: 5   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:37  Epoch: 5   Iter: 799   Class Loss: 0.01   Loss: 0.04
 29%|#    |ETA:   0:01:33  Epoch: 5   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:28  Epoch: 5   Iter: 998   Class Loss: 0.00   Loss: 0.03
 36%|#   |ETA:   0:01:24  Epoch: 5   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:20  Epoch: 5   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:15  Epoch: 5   Iter: 1298   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:11  Epoch: 5   Iter: 1399   Class Loss: 0.01   Loss: 0.04
 49%|#   |ETA:   0:01:06  Epoch: 5   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:02  Epoch: 5   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 5   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:54  Epoch: 5   Iter: 1798   Class Loss: 0.00   Loss: 0.04
 62%|##  |ETA:   0:00:49  Epoch: 5   Iter: 1898   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:45  Epoch: 5   Iter: 1997   Class Loss: 0.01   Loss: 0.04
 68%|##  |ETA:   0:00:41  Epoch: 5   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 71%|##  |ETA:   0:00:37  Epoch: 5   Iter: 2197   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:36  Epoch: 5   Iter: 2299   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:32  Epoch: 5   Iter: 2398   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:24  Epoch: 5   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 5   Iter: 2598   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 5   Iter: 2699   Class Loss: 0.00   Loss: 0.04
 91%|### |ETA:   0:00:10  Epoch: 5   Iter: 2799   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 5   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 5   Iter: 2999   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:18  Epoch: 5   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:06  Epoch: 6   Iter: 98   Class Loss: 0.02   Loss: 0.06
  6%|     |ETA:   0:02:02  Epoch: 6   Iter: 199   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:01:58  Epoch: 6   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:55  Epoch: 6   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:51  Epoch: 6   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:45  Epoch: 6   Iter: 599   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:42  Epoch: 6   Iter: 697   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:37  Epoch: 6   Iter: 799   Class Loss: 0.00   Loss: 0.04
 29%|#    |ETA:   0:01:32  Epoch: 6   Iter: 897   Class Loss: 0.02   Loss: 0.06
 32%|#    |ETA:   0:01:48  Epoch: 6   Iter: 998   Class Loss: 0.00   Loss: 0.04
 36%|#   |ETA:   0:01:43  Epoch: 6   Iter: 1099   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:19  Epoch: 6   Iter: 1197   Class Loss: 0.00   Loss: 0.04
 42%|#   |ETA:   0:01:15  Epoch: 6   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:10  Epoch: 6   Iter: 1399   Class Loss: 0.14   Loss: 0.18
 49%|#   |ETA:   0:01:07  Epoch: 6   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:02  Epoch: 6   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 6   Iter: 1697   Class Loss: 0.05   Loss: 0.08
 58%|##  |ETA:   0:00:54  Epoch: 6   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:49  Epoch: 6   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:45  Epoch: 6   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 6   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 72%|##  |ETA:   0:00:37  Epoch: 6   Iter: 2198   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 6   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 6   Iter: 2399   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:23  Epoch: 6   Iter: 2499   Class Loss: 0.12   Loss: 0.15
 85%|### |ETA:   0:00:19  Epoch: 6   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 6   Iter: 2699   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:12  Epoch: 6   Iter: 2798   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:07  Epoch: 6   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 6   Iter: 2997   Class Loss: 0.00   Loss: 0.04
100%|####|Time:  0:02:20  Epoch: 6   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:08  Epoch: 7   Iter: 98   Class Loss: 0.01   Loss: 0.03
  6%|     |ETA:   0:02:03  Epoch: 7   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:01:58  Epoch: 7   Iter: 299   Class Loss: 0.00   Loss: 0.04
 13%|     |ETA:   0:01:55  Epoch: 7   Iter: 397   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:49  Epoch: 7   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:46  Epoch: 7   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:41  Epoch: 7   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:36  Epoch: 7   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:32  Epoch: 7   Iter: 897   Class Loss: 0.00   Loss: 0.03
 32%|#    |ETA:   0:01:27  Epoch: 7   Iter: 998   Class Loss: 0.00   Loss: 0.04
 36%|#   |ETA:   0:01:24  Epoch: 7   Iter: 1099   Class Loss: 0.00   Loss: 0.04
 39%|#   |ETA:   0:01:19  Epoch: 7   Iter: 1198   Class Loss: 0.00   Loss: 0.02
 42%|#   |ETA:   0:01:15  Epoch: 7   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:21  Epoch: 7   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:16  Epoch: 7   Iter: 1498   Class Loss: 0.00   Loss: 0.03
 52%|##  |ETA:   0:01:02  Epoch: 7   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 7   Iter: 1697   Class Loss: 0.00   Loss: 0.04
 58%|##  |ETA:   0:00:53  Epoch: 7   Iter: 1798   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:49  Epoch: 7   Iter: 1899   Class Loss: 0.00   Loss: 0.04
 65%|##  |ETA:   0:00:45  Epoch: 7   Iter: 1999   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:40  Epoch: 7   Iter: 2098   Class Loss: 0.00   Loss: 0.04
 71%|##  |ETA:   0:00:36  Epoch: 7   Iter: 2197   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 7   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:28  Epoch: 7   Iter: 2398   Class Loss: 0.00   Loss: 0.03
 81%|### |ETA:   0:00:23  Epoch: 7   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 7   Iter: 2597   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 7   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:10  Epoch: 7   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 7   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 7   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:16  Epoch: 7   Iter: 3052   Class Loss: 0.00   Loss: 0.03
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:28  Epoch: 8   Iter: 99   Class Loss: 0.00   Loss: 0.04
  6%|     |ETA:   0:02:20  Epoch: 8   Iter: 199   Class Loss: 0.01   Loss: 0.04
  9%|     |ETA:   0:01:57  Epoch: 8   Iter: 297   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:01:53  Epoch: 8   Iter: 398   Class Loss: 0.00   Loss: 0.03
 16%|     |ETA:   0:01:49  Epoch: 8   Iter: 499   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:01:45  Epoch: 8   Iter: 597   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:41  Epoch: 8   Iter: 698   Class Loss: 0.00   Loss: 0.03
 26%|#    |ETA:   0:01:37  Epoch: 8   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:32  Epoch: 8   Iter: 897   Class Loss: 0.00   Loss: 0.04
 32%|#    |ETA:   0:01:28  Epoch: 8   Iter: 998   Class Loss: 0.00   Loss: 0.04
 35%|#   |ETA:   0:01:23  Epoch: 8   Iter: 1097   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:18  Epoch: 8   Iter: 1198   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:15  Epoch: 8   Iter: 1299   Class Loss: 0.00   Loss: 0.03
 45%|#   |ETA:   0:01:10  Epoch: 8   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:06  Epoch: 8   Iter: 1498   Class Loss: 0.04   Loss: 0.07
 52%|##  |ETA:   0:01:16  Epoch: 8   Iter: 1598   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:01:11  Epoch: 8   Iter: 1699   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:58  Epoch: 8   Iter: 1799   Class Loss: 0.00   Loss: 0.03
 62%|##  |ETA:   0:00:56  Epoch: 8   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:47  Epoch: 8   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 8   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:36  Epoch: 8   Iter: 2199   Class Loss: 0.00   Loss: 0.03
 75%|### |ETA:   0:00:32  Epoch: 8   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:27  Epoch: 8   Iter: 2399   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:23  Epoch: 8   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 8   Iter: 2597   Class Loss: 0.00   Loss: 0.04
 88%|### |ETA:   0:00:15  Epoch: 8   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:10  Epoch: 8   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 8   Iter: 2898   Class Loss: 0.05   Loss: 0.08
 98%|### |ETA:   0:00:02  Epoch: 8   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:20  Epoch: 8   Iter: 3052   Class Loss: 0.00   Loss: 0.04
Recomputing the sampling probabilities
N/A%|     |ETA:  --:--:--  Epoch: nan   Iter: nan   Class Loss: nan   Loss: nan
  3%|      |ETA:   0:02:05  Epoch: 9   Iter: 98   Class Loss: 0.00   Loss: 0.03
  6%|     |ETA:   0:02:01  Epoch: 9   Iter: 198   Class Loss: 0.00   Loss: 0.03
  9%|     |ETA:   0:01:58  Epoch: 9   Iter: 299   Class Loss: 0.00   Loss: 0.03
 13%|     |ETA:   0:02:17  Epoch: 9   Iter: 398   Class Loss: 0.00   Loss: 0.04
 16%|     |ETA:   0:02:06  Epoch: 9   Iter: 497   Class Loss: 0.00   Loss: 0.03
 19%|     |ETA:   0:02:01  Epoch: 9   Iter: 598   Class Loss: 0.00   Loss: 0.03
 22%|#    |ETA:   0:01:40  Epoch: 9   Iter: 697   Class Loss: 0.00   Loss: 0.04
 26%|#    |ETA:   0:01:37  Epoch: 9   Iter: 799   Class Loss: 0.00   Loss: 0.03
 29%|#    |ETA:   0:01:32  Epoch: 9   Iter: 897   Class Loss: 0.01   Loss: 0.04
 32%|#    |ETA:   0:01:27  Epoch: 9   Iter: 998   Class Loss: 0.00   Loss: 0.04
 36%|#   |ETA:   0:01:23  Epoch: 9   Iter: 1099   Class Loss: 0.00   Loss: 0.03
 39%|#   |ETA:   0:01:19  Epoch: 9   Iter: 1197   Class Loss: 0.00   Loss: 0.03
 42%|#   |ETA:   0:01:15  Epoch: 9   Iter: 1298   Class Loss: 0.00   Loss: 0.04
 45%|#   |ETA:   0:01:10  Epoch: 9   Iter: 1397   Class Loss: 0.00   Loss: 0.03
 49%|#   |ETA:   0:01:06  Epoch: 9   Iter: 1498   Class Loss: 0.00   Loss: 0.04
 52%|##  |ETA:   0:01:02  Epoch: 9   Iter: 1599   Class Loss: 0.00   Loss: 0.03
 55%|##  |ETA:   0:00:58  Epoch: 9   Iter: 1697   Class Loss: 0.00   Loss: 0.03
 58%|##  |ETA:   0:00:53  Epoch: 9   Iter: 1798   Class Loss: 0.07   Loss: 0.11
 62%|##  |ETA:   0:00:49  Epoch: 9   Iter: 1899   Class Loss: 0.00   Loss: 0.03
 65%|##  |ETA:   0:00:44  Epoch: 9   Iter: 1997   Class Loss: 0.00   Loss: 0.03
 68%|##  |ETA:   0:00:41  Epoch: 9   Iter: 2098   Class Loss: 0.00   Loss: 0.03
 72%|##  |ETA:   0:00:36  Epoch: 9   Iter: 2199   Class Loss: 0.00   Loss: 0.04
 75%|### |ETA:   0:00:37  Epoch: 9   Iter: 2298   Class Loss: 0.00   Loss: 0.03
 78%|### |ETA:   0:00:33  Epoch: 9   Iter: 2399   Class Loss: 0.00   Loss: 0.04
 81%|### |ETA:   0:00:23  Epoch: 9   Iter: 2497   Class Loss: 0.00   Loss: 0.03
 85%|### |ETA:   0:00:19  Epoch: 9   Iter: 2598   Class Loss: 0.00   Loss: 0.03
 88%|### |ETA:   0:00:15  Epoch: 9   Iter: 2698   Class Loss: 0.00   Loss: 0.03
 91%|### |ETA:   0:00:10  Epoch: 9   Iter: 2797   Class Loss: 0.00   Loss: 0.03
 94%|### |ETA:   0:00:06  Epoch: 9   Iter: 2898   Class Loss: 0.00   Loss: 0.03
 98%|### |ETA:   0:00:02  Epoch: 9   Iter: 2999   Class Loss: 0.00   Loss: 0.03
100%|####|Time:  0:02:18  Epoch: 9   Iter: 3052   Class Loss: 0.00   Loss: 0.03
100% (97 of 97) |########################| Elapsed Time: 0:01:06 Time:  0:01:06
N/A% (0 of 72) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male lighter: 1.0
100% (72 of 72) |########################| Elapsed Time: 0:00:49 Time:  0:00:49
N/A% (0 of 78) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
female lighter: 0.986111111111
100% (78 of 78) |########################| Elapsed Time: 0:00:51 Time:  0:00:51
N/A% (0 of 71) |                         | Elapsed Time: 0:00:00 ETA:  --:--:--
male darker: 0.897435897436
100% (71 of 71) |########################| Elapsed Time: 0:00:48 Time:  0:00:48
female darker: 0.985915492958